🔬Data Science: Pattern Recognition (Definitions)

"The categorization of patterns in some domain into meaningful classes. A pattern usually has the form of a vector of measurement values." (Guido Deboeck & Teuvo Kohonen (Eds), "Visual Explorations in Finance with Self-Organizing Maps 2nd Ed.", 2000)

"in the most general sense the same as artificial perception." (Teuvo Kohonen, "Self-Organizing Maps" 3rd Ed., 2001)

"The operation and design of systems that recognize patterns in data." (Craig F Smith & H Peter Alesso, "Thinking on the Web: Berners-Lee, Gödel and Turing", 2008)

"Research area that enclose the development of methods and automatized techniques for identification and classification of samples in specific groups, in accordance with representative characteristics." (Paulo E Ambrósio, "Artificial Intelligence in Computer-Aided Diagnosis",  Encyclopedia of Artificial Intelligence, 2009)

"The process of identifying patterns in data via algorithms to make predictions within a subject area." (Jason Williamson, Getting a Big Data Job For Dummies, 2015)

"A branch of machine learning that recognizes and separates the patterns of one class from the other." (Mridusmita Sharma & Kandarpa K Sarma, "Soft-Computational Techniques and Spectro-Temporal Features for Telephonic Speech Recognition: An Overview and Review of Current State of the Art", 2016)

"A pattern is a particular configuration of data; for example, ‘A’ is a composition of three strokes. Pattern recognition is the detection of such patterns." (Ethem Alpaydın, "Machine learning : the new AI", 2016)

"Pattern Recognition in the discipline which tries to find the classes in the datasets of the various applications and it is the major building block of artificially intelligent systems." (Vandana M Ladwani, "Support Vector Machines and Applications", 2017)

"identifying patterns in data via algorithms to make predictions of new data coming from the same source." (Analytics Insight)

🔬Data Science: Markov Process (Definitions)

"A Markov process is any stochastic process in which the future development is completely determined by the present state and not at all by the way in which the present state arose." (David B MacNeil, "Modern Mathematics for the Practical Man", 1963)

"A Markov process is a stochastic process in which present events depend on the past only through some finite number of generations. In a first-order Markov process, the influential past is limited to a single earlier generation: the present can be fully accounted for by the immediate past." (Manfred Schroeder, "Fractals, Chaos, Power Laws Minutes from an Infinite Paradise", 1990)

"stochastic process in which the new state of a system depends on the previous state only (or more generally, on a finite set of previous states)." (Teuvo Kohonen, "Self-Organizing Maps" 3rd Ed., 2001)

"A stochastic process in which the transition probabilities can be estimated on the basis of first order data. Such a process is also stationary in that probability estimates do not change across the sample (generally across time)." (W David Penniman,"Historic Perspective of Log Analysis", 2009)

"Stochastic process in which the new state of a system depends on the previous state or a finite set of previous states." (Patrick Rousset & Jean-Francois Giret, "A Longitudinal Analysis of Labour Market Data with SOM" Encyclopedia of Artificial Intelligence, 2009)

"A stochastic process where the probabilities of the events depend on the previous event only." (Michael M Richter, "Business Processes, Dynamic Contexts, Learning", 2014)

"A Markov chain (or Markov process) is a system containing a finite number of distinct states S1,S2,…,Sn on which steps are performed such that: (1) At any time, each element of the system resides in exactly one of the states. (2) At each step in the process, elements in the system can move from one state to another. (3) The probabilities of moving from state to state are fixed - that is, they are the same at each step in the process." (Stephen Andrilli & David Hecker, [in [Elementary Linear Algebra] 5th Ed.), 2016)

[hidden Markov model:] "A hidden Markov model is a technique for modeling sequences using a hidden state that only uses the previous part of the sequence." (Alex Thomas, "Natural Language Processing with Spark NLP", 2020)

[Markov decision process:] "A stochastic dynamic program, whereby for each policy the resulting state variables comprise a Markov process (a stochastic process with the property that the conditional probability of a transition to any state depends only on the current state, and not on previous states)." (Mathematical Programming Glossary)

🔬Data Science: Time Series (Definitions)

"A time series may be defined as a collection of readings belonging to different time periods, of some economic variable or composite of variables." (Ya-lun Chou, "Statistical Analysis", 1969)

"It is composed of a sequence of values, where each value corresponds to a time instance. The length remains constant." (Maria Kontaki et al, "Similarity Search in Time Series",  2009)

"a time series is a sequence of data points, measured typically at successive times, spaced at time intervals." (Yong Yu et al, "Applications of Evolutionary Neural Networks for Sales Forecasting of Fashionable Products", 2010)

"A sequence of numerical values of a variable obtained at some regular/uniform intervals of time or at non uniform intervals of time." (Mofazzal H Khondekar et al, "Soft Computing Based Statistical Time Series Analysis, Characterization of Chaos Theory, and Theory of Fractals", 2013)

"A series of values of a quantity obtained at successive times, often with equal intervals between them." (Dima Alberg & Zohar Laslo, "Segmenting Big Data Time Series Stream Data", 2014) 

"An ordered sequence of values that correspond to a variable that is typically sampled at a uniform sampling rate. Time series prediction is intended to make estimations about the future values of the series." (Fernando Mateo et al, "Forecasting Techniques for Energy Optimization in Buildings", 2015)

"A sequence of data points consisting of consecutive measurements that are made over a time interval." (Vasileios Zois, "Querying of Time Series for Big Data Analytics", 2016)

"A series of values of a quantity obtained at successive times, often with equal intervals between them." (Dima Alberg, "Big Data Time Series Stream Data Segmentation Methods", Encyclopedia of Information Science and Technology, 2018)

"A time series is a sequence of values, usually taken in equally spaced intervals. […] Essentially, anything with a time dimension, measured in regular intervals, can be used for time series analysis." (Andy Kriebel & Eva Murray, "#MakeoverMonday: Improving How We Visualize and Analyze Data, One Chart at a Time", 2018)

"A series of data points indexed (or listed or graphed) in time order. Most commonly, a time series is a sequence taken at successive equally spaced points in time." (Gurpreet Kaur & Akriti Gupta, "India-BIMSTEC Bilateral Trade Activities: A Gravity Model Approach", 2020)

"Time series is a series of data points that are listed in time order." (Siyu Shi, "Introduction to Python and Its Statistical Applications", 2020)

"A set of successive observations collected generally at the same interval, named period." (Oumayma Bounouh et al, "Investigating the Pixel Quality Influence on Forecasting Vegetation Change Dynamics: Application Case of Tunisian Olive Sites", 2021)

🔬Data Science: Recurrent Neural Network [RNN] (Definitions)

"A neural net with feedback connections, such as a BAM, Hopfield net, Boltzmann machine, or recurrent backpropagation net. In contrast, the signal in a feedforward neural net passes from the input units (through any hidden units) to the output units." (Laurene V Fausett, "Fundamentals of Neural Networks: Architectures, Algorithms, and Applications", 1994)

"A neural network topology where the units are connected so that inputs signals flow back and forth between the neural processing units until the neural network settles down. The outputs are then read from the output units." (Joseph P Bigus, "Data Mining with Neural Networks: Solving Business Problems from Application Development to Decision Support", 1996)

"Networks with feedback connections from neurons in one layer to neurons in a previous layer." (Nikola K Kasabov, "Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering", 1996)

"RNN topology involves backward links from output to the input and hidden layers." (Siddhartha Bhattacharjee et al, "Quantum Backpropagation Neural Network Approach for Modeling of Phenol Adsorption from Aqueous Solution by Orange Peel Ash", 2013)

"Neural network whose feedback connections allow signals to circulate within it." (Terrence J Sejnowski, "The Deep Learning Revolution", 2018)

"An RNN is a special kind of neural network used for modeling sequential data." (Alex Thomas, "Natural Language Processing with Spark NLP", 2020)

"A recurrent neural network (RNN) is a class of artificial neural networks where connections between nodes form a directed graph along a temporal sequence. This allows it to exhibit temporal dynamic behavior." (Udit Singhania & B. K. Tripathy, "Text-Based Image Retrieval Using Deep Learning", 2021)

"A RNN [Recurrent Neural Network] models sequential interactions through a hidden state, or memory. It can take up to N inputs and produce up to N outputs. For example, an input sequence may be a sentence with the outputs being the part-of-speech tag for each word (N-to-N). An input could be a sentence, and the output a sentiment classification of the sentence (N-to-1). An input could be a single image, and the output could be a sequence of words corresponding to the description of an image (1-to-N). At each time step, an RNN calculates a new hidden state ('memory') based on the current input and the previous hidden state. The 'recurrent' stems from the facts that at each step the same parameters are used and the network performs the same calculations based on different inputs." (Wild ML)

"Recurrent Neural Network (RNN) refers to a type of artificial neural network used to understand sequential information and predict follow-on probabilities. RNNs are widely used in natural language processing, with applications including language modeling and speech recognition." (Accenture)

🔬Data Science: Generative Adversarial Network (Definitions)

"A category of deep learning neural networks that are composed of two competitive neural networks together." (Dulani Meedeniya & Iresha Rubasinghe, "A Review of Supportive Computational Approaches for Neurological Disorder Identification", 2020) 

"A powerful machine learning technique made up of two learning systems that compete with each other in a game-like fashion. Features of the winning system are 'genetically' added to the loser along with random mutations. GANs teach themselves through this 'survival of the fittest' evolutionary model. They 'generate' new solutions through many, often millions, of generations." (Scott R Garrigan, "Frameworks for Integration of Future-Oriented Computational Thinking in K-12 Schools", 2020)

"An artificial intelligence process that includes a 'generator' that produces samples, and a 'discriminator' that differentiates between computer-generated samples and samples derived from 'real-world' sources." (Keram Malicki-Sanchez, "Out of Our Minds: Ontology and Embodied Media in a Post-Human Paradigm", 2020)

"Machine learning framework in which two neural networks compete against each other to win within a gaming environment using a supervised learning pattern." (Jose A R Pinheiro, "Contemporary Imagetics and Post-Images in Digital Media Art: Inspirational Artists and Current Trends (1948-2020)", 2020)

"It refers to a type of neural network that consists of a generative and a discriminative network that contest with each other especially in a game scenario. They are used to generate new data that are statistically similar to the training data." (Vijayaraghavan Varadharajan & J Rian Leevinson, "Next Generation of Intelligent Cities: Case Studies from Europe", 2021)

"A generative adversarial network, or GAN, is a deep neural network framework which is able to learn from a set of training data and generate new data with the same characteristics as the training data." (Thomas Wood)

🔬Data Science: Semi-supervised Learning (Definitions)

"machine learning technique that uses both labelled and unlabelled data for constructing the model." (Óscar Pérez & Manuel Sánchez-Montañés, "Class Prediction in Test Sets with Shifted Distributions", 2009)

"The set of learning algorithms in which the samples in training dataset are all unlabelled." (Jun Jiang & Horace H S Ip, "Active Learning with SVM, Encyclopedia of Artificial Intelligence", 2009) 

"Learning to label new data using both labeled training data plus unlabeled data." (Jesse Read & Albert Bifet, "Multi-Label Classification", 2014)

"A method of empirical concept learning from unlabeled data. The task is to build a model that finds groups of similar examples or that finds dependencies between attribute-value tuples." (Petr Berka, "Machine Learning", 2015)

"Combines the methodology of the supervised learning to process the labeled data with the unsupervised learning to compute the unlabeled data." (Nuno Pombo et al, "Machine Learning Approaches to Automated Medical Decision Support Systems", 2015)

"Estimation of the parameters of a model considering only un-labeled data and without the help of human experts." (Manuel Martín-Merino, "Semi-Supervised Dimension Reduction Techniques to Discover Term Relationships", 2015)

"In this category either the model is developed in such a way that either there are labels exist for all kind of observations or there is no label exist." (Neha Garg & Kamlesh Sharma, "Machine Learning in Text Analysis", 2020)

"It is a machine learning algorithm in which the machine learns from both labeled and unlabeled instances to build a model for predicting the class of unlabeled instances." (Gunjan Ansari et al, "Natural Language Processing in Online Reviews", 2021)

"Semi-supervised learning aims at labeling a set of unlabelled data with the help of a small set of labeled data." (Hari K Kondaveeti et al, "Deep Learning Applications in Agriculture: The Role of Deep Learning in Smart Agriculture", 2021)

"The semi-supervised learning combines both supervised and unsupervised learning algorithms." (M Govindarajan, "Big Data Mining Algorithms", 2021)

🔬Data Science: Perceptron (Definitions)

"the term is often used to refer to a single layer pattern classification network with linear threshold units" (Laurene V Fausett, "Fundamentals of Neural Networks: Architectures, Algorithms, and Applications", 1994)

"adaptive element for multilayer feedforward networks introduced by Rosenblatt" (Teuvo Kohonen, "Self-Organizing Maps" 3rd Ed., 2001)

"An early theoretical model of the neuron developed by Rosenblatt (1958) that was the first to incorporate a learning rule. The term is also used as a generic label for all trained feed-forward networks, which is often referred to collectively as multilayer perceptron networks." (David Scarborough & Mark J Somers, "Neural Networks in Organizational Research: Applying Pattern Recognition to the Analysis of Organizational Behavior", 2006)

"A type of binary classifier that maps its inputs (a vector of real type) to an output value (a scalar real type). The perceptron may be considered as the simplest model of feed-forward neural network, as the inputs directly feeding the output units through weighted connections." )Crescenzio Gallo, "Artificial Neural Networks Tutorial", 2015)

"A perceptron is a type of a neural network organized into layers where each layer receives connections from units in the previous layer and feeds its output to the units of the layer that follow." (Ethem Alpaydın, "Machine learning : the new AI", 2016)

"Perceptron is a learning algorithm which is used to learn the decision boundary for linearly separable data." Vandana M Ladwani, "Support Vector Machines and Applications", 2017)

"A simple neural network model consisting of one unit and inputs with variable weights that can be trained to classify inputs into categories." (Terrence J Sejnowski, "The Deep Learning Revolution", 2018)

"The simplest form of artificial neural network, a basic operational unit which employs supervised learning. It is used to classify data into two classes." (Gaetano B Ronsivalle & Arianna Boldi, "Artificial Intelligence Applied: Six Actual Projects in Big Organizations", 2019)

"A perceptron is a single-layer neural network. It includes input values, weights and bias, net sum, and an activation function." (Prisilla Jayanthi & Muralikrishna Iyyanki, "Deep Learning Techniques for Prediction, Detection, and Segmentation of Brain Tumors", 2020)

"The basic unit of a neural network that encodes inputs from neurons of the previous layer using a vector of weights or parameters associated with the connections between perceptrons." Mário P Véstias, "Deep Learning on Edge: Challenges and Trends", 2020)

"these are machine learning algorithms that undertake supervised learning of functions called binary classifiers which decide whether or not an input, usually identified with a vector of numbers, belongs to a particular class." (Hari Kishan Kondaveeti et al, "Deep Learning Applications in Agriculture: The Role of Deep Learning in Smart Agriculture", 2021)

🔬Data Science: Convolutional Neural Network [CNN] (Definitions)

"A multi layer neural network similar to artificial neural networks only differs in its architecture and mainly built to recognize visual patterns from image pixels." (Nishu Garg et al, "An Insight Into Deep Learning Architectures, Latent Query Features", 2018)

"In machine learning, a convolutional neural network is a class of deep, feed-forward artificial neural networks that has successfully been applied to analyzing visual imagery. CNNs use a variation of multilayer perceptrons designed to require minimal preprocessing. They are also known as shift invariant or space invariant artificial neural networks (SIANN), based on their shared-weights architecture and translation invariance characteristics." (V E Jayanthi, "Automatic Detection of Tumor and Bleed in Magnetic Resonance Brain Images", 2018)

"A special type of feed-forward neural network optimized for image data processing. The key features of CNN architecture include sharing weights, using pooling layers, implementing deep structures with multiple hidden layers." (Lyudmila N. Tuzova et al, "Teeth and Landmarks Detection and Classification Based on Deep Neural Networks", 2019)

"A type of artificial neural networks, which uses a set of filters with tunable (learnable) parameters to extract local features from the input data." (Sergei Savin & Aleksei Ivakhnenko, "Enhanced Footsteps Generation Method for Walking Robots Based on Convolutional Neural Networks", 2019) 

"A convolutional neural network (CNN) is a type of artificial neural network used in image recognition and processing that is specifically designed to process pixel data by means of learnable filters." (Loris Nanni et al, "Digital Recognition of Breast Cancer Using TakhisisNet: An Innovative Multi-Head Convolutional Neural Network for Classifying Breast Ultrasonic Images", 2020)

"A convolutional neural network (CNN) is a type of artificial neural network used in image recognition and processing that is specifically designed to process pixel data. CNNs are powerful image processing, artificial intelligence (AI) that use deep learning to perform both generative and descriptive tasks, often using machine vision that includes image and video recognition, along with recommender systems and natural language processing (NLP)." (Mohammad F Hashmi et al, "Subjective and Objective Assessment for Variation of Plant Nitrogen Content to Air Pollutants Using Machine Intelligence", 2020)

"A neural network with a convolutional layer which does the mathematical operation of convolution in addition to the other layers of deep neural network." (S Kayalvizhi & D Thenmozhi, "Deep Learning Approach for Extracting Catch Phrases from Legal Documents", 2020)

"A special type of neural networks used popularly to analyze photography and imagery." (Murad Al Shibli, "Hybrid Artificially Intelligent Multi-Layer Blockchain and Bitcoin Cryptology", 2020)

"In deep learning, a convolutional neural network is a class of deep neural networks, most commonly applied to analyzing visual imagery. CNNs use a variation of multilayer perceptrons designed to require minimal preprocessing." (R Murugan, "Implementation of Deep Learning Neural Network for Retinal Images", 2020)

"A class of deep neural networks applied to image processing where some of the layers apply convolutions to input data." (Mário P Véstias, "Convolutional Neural Network", 2021)

"A convolution neural network is a kind of ANN used in image recognition and processing of image data." (M Srikanth Yadav & R Kalpana, "A Survey on Network Intrusion Detection Using Deep Generative Networks for Cyber-Physical Systems", 2021)

"A multi-layer neural network similar to artificial neural networks only differs in its architecture and mainly built to recognize visual patterns from image pixels." (Udit Singhania & B K Tripathy, "Text-Based Image Retrieval Using Deep Learning", 2021) 

"A type of deep learning algorithm commonly applied in analyzing image inputs." (Jinnie Shin et al, "Automated Essay Scoring Using Deep Learning Algorithms", 2021)

"It is a class of deep neural networks, most commonly applied to analyzing visual imagery." (Sercan Demirci et al, "Detection of Diabetic Retinopathy With Mobile Application Using Deep Learning", 2021)

"They are a class of deep neural networks that are generally used to analyze image data. They use convolution instead of simple matrix multiplication in a few layers of the network. They have shared weights architecture and have translation invariant characteristics." Vijayaraghavan Varadharajan & J Rian Leevinson, "Next Generation of Intelligent Cities: Case Studies from Europe", 2021) 

🔬Data Science: Boltzmann Machine (Definitions)

[Boltzmann machine (with learning):] "A net that adjusts its weights so that the equilibrium configuration of the net will solve a given problem, such as an encoder problem" (David H Ackley et al, "A learning algorithm for boltzmann machines", Cognitive Science Vol. 9 (1), 1985)

[Boltzmann machine (without learning):] "A class of neural networks used for solving constrained optimization problems. In a typical Boltzmann machine, the weights are fixed to represent the constraints of the problem and the function to be optimized. The net seeks the solution by changing the activations (either 1 or 0) of the units based on a probability distribution and the effect that the change would have on the energy function or consensus function for the net." (David H Ackley et al, "A learning algorithm for boltzmann machines", Cognitive Science Vol. 9 (1), 1985)

"neural-network model otherwise similar to a Hopfield network but having symmetric interconnects and stochastic processing elements. The input-output relation is optimized by adjusting the bistable values of its internal state variables one at a time, relating to a thermodynamically inspired rule, to reach a global optimum." (Teuvo Kohonen, "Self-Organizing Maps 3rd" Ed., 2001)

"A neural network model consisting of interacting binary units in which the probability of a unit being in the active state depends on its integrated synaptic inputs." (Terrence J Sejnowski, "The Deep Learning Revolution", 2018)

"An unsupervised network that maximizes the product of probabilities assigned to the elements of the training set." (Mário P Véstias, "Deep Learning on Edge: Challenges and Trends", 2020)

"Restricted Boltzmann machine (RBM) is an undirected graphical model that falls under deep learning algorithms. It plays an important role in dimensionality reduction, classification and regression. RBM is the basic block of Deep-Belief Networks. It is a shallow, two-layer neural networks. The first layer of the RBM is called the visible or input layer while the second is the hidden layer. In RBM the interconnections between visible units and hidden units are established using symmetric weights." (S Abirami & P Chitra, "The Digital Twin Paradigm for Smarter Systems and Environments: The Industry Use Cases", Advances in Computers, 2020)

"A deep Boltzmann machine (DBM) is a type of binary pairwise Markov random field (undirected probabilistic graphical model) with multiple layers of hidden random variables." (Udit Singhania & B. K. Tripathy, "Text-Based Image Retrieval Using Deep Learning",  2021) 

"A Boltzmann machine is a neural network of symmetrically connected nodes that make their own decisions whether to activate. Boltzmann machines use a straightforward stochastic learning algorithm to discover “interesting” features that represent complex patterns in the database." (DeepAI) [source]

"Boltzmann Machines is a type of neural network model that was inspired by the physical process of thermodynamics and statistical mechanics. [...] Full Boltzmann machines are impractical to train, which is one of the reasons why a limited form, called the restricted Boltzmann machine, is used." (Accenture)

"RBMs [Restricted Boltzmann Machines] are a type of probabilistic graphical model that can be interpreted as a stochastic artificial neural network. RBNs learn a representation of the data in an unsupervised manner. An RBN consists of visible and hidden layer, and connections between binary neurons in each of these layers. RBNs can be efficiently trained using Contrastive Divergence, an approximation of gradient descent." (Wild ML)

🔬Data Science: Natural Language Processing [NLP] (Definitions)

"Using software to 'understand' the meaning contained within texts. Everyday speech is broken down into patterns. Typically, these systems employ syntactic analysis to infer the semantic meaning embedded in documents. NLP identifies patterns in sample texts and makes predictions about unseen texts." (Craig F Smith & H Peter Alesso, "Thinking on the Web: Berners-Lee, Gödel and Turing", 2008)

"Use of computers to interpret and manipulate words as part of a language." (Dougal Hutchison, "Automated Essay Scoring Systems", 2009)

"It is a subfield of Computational Linguistics (i.e. the field that researches linguistics phenomena that occur in digital data), whose focus is on how to build automatic systems able to interpret/generate information in natural language." (Diana Pérez-Marín et al, "Adaptive Computer Assisted Assessment", 2010)

"the notion that the context of text can be inferred from the text itself." (Daniel Linstedt & W H Inmon, "Data Architecture: A Primer for the Data Scientist", 2014)

"An area of computer science involved with the computational study of human languages." (Jason Williamson, "Getting a Big Data Job For Dummies", 2015)

"Similarly to text mining, NLP is a multidisciplinary research field of computer science, artificial intelligence, and linguistics. However, it mainly focuses on the interaction between computers and human languages." (Hamid R Arabnia et al, "Application of Big Data for National Security", 2015)

"Natural Language Processing is prevalently used to analyse the text or speech in order to make machine understand the words like human." (Anumeera Balamurali & Balamurali Ananthanarayanan,"Develop a Neural Model to Score Bigram of Words Using Bag-of-Words Model for Sentiment Analysis", 2020)

 "Natural language processing is the ability of computer program to understand human language as it is spoken or handwritten." (Neha Garg & Kamlesh Sharma, "Machine Learning in Text Analysis", 2020)

"NLP is a field of computer science and linguistics focused on techniques and algorithms for processing data, continuing natural language." (Alex Thomas, "Natural Language Processing with Spark NLP", 2020)

"NLP is a Linguistic approach to interact with human language and computer. This field comes under Artificial Intelligence and Computer Science." (Sayani Ghosal & Amita Jain, "Research Journey of Hate Content Detection From Cyberspace", 2021)

"a field of computer science involved with interactions between computers and human languages." (Analytics Insight)

"is a field of computer science, with the goal to understand or generate human languages, either in text or speech form. There are two primary sub fields of NLP, Natural Language Understanding (NLU), and Natural Language Generation (NLG)." (Accenture)

🔬Data Science: Learning (Definitions)

"Procedures for modifying the weights on the connection links in a neural net (also known as training algorithms, learning rules)." (Laurene V Fausett, "Fundamentals of Neural Networks: Architectures, Algorithms, and Applications", 1994)

"In the simplest form: self-adaptation at the processing element level. Weighted connections between processing elements or weights are adjusted to achieve specific results, eliminating the need for writing a specific algorithm for each problem. More generally: change of rules or behavior for a certain objective." (Guido J Deboeck and Teuvo Kohonen, "Visual explorations in finance with self-organizing maps", 2000)

"generic name for all behavioral changes that depend on experiences and improve the performance of a system. In a more restricted sense learning is identical with adaptation, especially selective modification of parameters of a system." (Teuvo Kohonen, "Self-Organizing Maps" 3rd Ed., 2001)

"A process whereby a training set of examples is used to generate a model that understands and generalizes the relationship between the descriptor variables and one or more response variables." (Glenn J Myatt, "Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining", 2006)

"The process of automatically finding relations between inputs and outputs given examples of that relation." (Craig F Smith & H Peter Alesso, "Thinking on the Web: Berners-Lee, Gödel and Turing", 2008)

"An essential operation of acquiring, processing and storing information required by any intelligent system for evolution." (T R Gopalakrishnan Nair, "Cognitive Approaches for Intelligent Networks", 2015)

"Adaptation of synaptic weights of a neural network as training progresses, usually with the objective of minimizing a cost function." (Anand Parey & Amandeep S Ahuja, "Application of Artificial Intelligence to Gearbox Fault Diagnosis: A Review", 2016)

"Algorithm for changing the parameters of a function based on examples. Learning algorithms are said to be “supervised” when both inputs and desired outputs are given or “unsupervised” when only inputs are given. Reinforcement learning is a special case of a supervised learning algorithm when the only feedback is a reward for good performance." (Terrence J Sejnowski, "The Deep Learning Revolution", 2018)

"A phase in the machine learning methods that aggregates some information about the state actions for using in the future predictions of the events." (Derya Yiltas-Kaplan, "The Usage Analysis of Machine Learning Methods for Intrusion Detection in Software-Defined Networks", 2019)

🔬Data Science: Type I Error (Definitions)

"Within a hypothesis test, a type I error is the error of incorrectly rejecting a null hypothesis when it is true." (Glenn J Myatt, "Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining", 2006)

"A type of error used in hypothesis testing that arises when incorrectly rejecting the null hypothesis, although it is actually true. Thus, based on the test statistic, the final conclusion rejects the Null hypothesis, but in truth it should be accepted. Type I error equates to the alpha (α) or significance level, whereby the generally accepted default is 5%." (Lynne Hambleton, "Treasure Chest of Six Sigma Growth Methods, Tools, and Best Practices", 2007)

"A term that refers to incorrectly rejecting a null hypothesis. It is also sometimes termed a false positive. It is used when an outcome is incorrectly identified as having happened, such as when a customer is incorrectly identified as having committed fraud." (Evan Stubbs, "Delivering Business Analytics: Practical Guidelines for Best Practice", 2013)

"Rejection of the null hypothesis when it's true." (Geoff Cumming, "Understanding The New Statistics", 2013)

"Probability of rejecting the null hypothesis when the null hypothesis is true." (K  N Krishnaswamy et al, "Management Research Methodology: Integration of Principles, Methods and Techniques", 2016)

"Probability of rejecting the null hypothesis when it's true." (Geoff Cumming, "Understanding The New Statistics", 2013)

🔬Data Science: Unsupervised Learning (Definitions)

"A means of modifying the weights of a neural net without specifying the desired output for any input patterns. Used in self-organizing neural nets for clustering data, extracting principal components, or curve fitting." (Laurene V Fausett, "Fundamentals of Neural Networks: Architectures, Algorithms, and Applications", 1994)

"Learning in which no teacher is used to show the correct response to a given input stimulus; the system must organize itself purely on the basis of the input stimuli it receives. Often synonymous with clustering." (Guido J Deboeck & Teuvo Kohonen, "Visual explorations in finance with self-organizing maps", 2000)

"learning without a priori knowledge about the classification of samples; learning without a teacher. Often the same as formation of clusters, where after these clusters can be labeled. Also optimal allocation of computing resources when only unlabeled, unclassified data are input." (Teuvo Kohonen, "Self-Organizing Maps 3rd Ed.", 2001)

"Analysis methods that do not use any data to guide the technique operations." (Glenn J Myatt, "Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining", 2006)

"Learning techniques that group instances without a pre-specified dependent attribute. Clustering algorithms are usually unsupervised methods for grouping data sets." (Lluís Formiga & Francesc Alías, "GTM User Modeling for aIGA Weight Tuning in TTS Synthesis", Encyclopedia of Artificial Intelligence, 2009)

"Method of machine learning where a model is fit to observations. It is distinguished from supervised learning by the fact that there is no a priori output." (Soledad Delgado et al, "Growing Self-Organizing Maps for Data Analysis", Encyclopedia of Artificial Intelligence, 2009)

"The type of learning that occurs when algorithms adjust the weights in a neural network by reference to a training data set that includes input variables only. Unsupervised learning algorithms attempt to locate clusters in the input data." (Robert Nisbet et al, "Handbook of statistical analysis and data mining applications", 2009)

"Treats all variables the same way so as to determine the different classes based on diverse features observed in the collection of unlabeled data that encompass the sample set. It is assumed that the user is unaware of the classes due to the lack of information sufficiently available." (Nuno Pombo et al, "Machine Learning Approaches to Automated Medical Decision Support Systems", 2015)

"Unsupervised learning refers to a machine learning approach that uses inferential statistical modeling algorithms to discover rather than detect patterns or similarities in data. An unsupervised learning system can identify new patterns, instead of trying to match a set of patterns it encountered during training." (Judith S Hurwitz, "Cognitive Computing and Big Data Analytics", 2015)

"In unsupervised learning, the program gets the same items but has to come up with the categories on its own by discovering the underlying correlations between the items; that is why unsupervised learning is sometimes called statistical pattern recognition." (Robert J Glushko, "The Discipline of Organizing: Professional Edition, 4th Ed", 2016)

"A form of machine learning in which the goal is to identify regularities in the data. These regularities may include clusters of similar instances within the data or regularities between attributes. In contrast to supervised learning, in unsupervised learning no target attribute is defined in the data set." (John D Kelleher & Brendan Tierney, "Data science", 2018)

"Unsupervised learning identifies hidden patterns or intrinsic structures in the data. It is used to draw conclusions from datasets composed of labeled unacknowledged input data." (Soraya Sedkaoui, "Big Data Analytics for Entrepreneurial Success", 2018)

"Unsupervised learning or clustering is a way of discovering hidden structures in unlabeled data. Clustering algorithms aim to discover latent patterns in unlabeled data using features to organize instances into meaningfully dissimilar groups." (Benjamin Bengfort et al, "Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning", 2018)

"A particular form of learning process that takes place without supervision and that affects the training of an artificial neural networks." (Gaetano B Ronsivalle & Arianna Boldi, "Artificial Intelligence Applied: Six Actual Projects in Big Organizations", 2019)

"In this learning, the model does not require labeled data for training. The model learns the nature of data and does predictions." (Aman Kamboj et al, "Ear Localizer: A Deep-Learning-Based Ear Localization Model for Side Face Images in the Wild", 2019)

"A class of machine learning techniques designed to identify features and patterns in data. There is no mapping function to be learned or output values to be achieved. Cluster analysis is an example of unsupervised learning." (Timofei Bogomolov et al, "Identifying Patterns in Fresh Produce Purchases: The Application of Machine Learning Techniques", 2020)

"Unsupervised algorithms mean that a program is provided with some collection of data, with no predetermined dataset being available." (Neha Garg & Kamlesh Sharma, "Machine Learning in Text Analysis", 2020)

"A machine learning technique that involves providing a machine with data that is not labeled, instead allowing for the machine to learn by association." (Sujata Ramnarayan, "Marketing and Artificial Intelligence: Personalization at Scale", 2021)

"Unsupervised Learning aims at inferring the given unlabelled data using a different type of structures present in the data points." (Hari K Kondaveeti et al, "Deep Learning Applications in Agriculture: The Role of Deep Learning in Smart Agriculture", 2021)

"Unsupervised Learning is a type of machine learning in which the algorithm does not need the data with pre-defined labels. Unsupervised machine learning instead categorizes entries within datasets by examining similarities or anomalies and then grouping different entries accordingly." (Accenture)

🔬Data Science: Training Set/Dataset (Definitions)

"set of data used as inputs in an adaptive process that teaches a neural network." (Teuvo Kohonen, "Self-Organizing Maps" 3rd Ed., 2001)

"A set of observations that are used in creating a prediction model." (Glenn J Myatt, "Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining", 2006)

"the training set is composed by all labelled examples that are provided for constructing a classifier. The test set is composed by the new unlabelled patterns whose classes should be predicted by the classifier." (Óscar Pérez & Manuel Sánchez-Montañés, "Class Prediction in Test Sets with Shifted Distributions", 2009)

"A collection of data whose purpose is to be analyzed to discover patterns that can then be applied to other data sets." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"A training set for supervised learning is taken from the labeled instances. The remaining instances are used for validation." (Robert J Glushko, "The Discipline of Organizing: Professional Edition" 4th Ed., 2016)

"A set of known and predictable data used to train a data mining model." (Microsoft, "SQL Server 2012 Glossary", 2012)

"In data mining, a sample of data used at each iteration of the training process to evaluate the model fit." (Meta S Brown, "Data Mining For Dummies", 2014)

"Training Data is the data used to train a machine learning algorithm. Generally, data in machine learning is divided into three datasets: training, validation and testing data. In general, the more accurate and comprehensive training data is, the better the algorithm or classifier will perform." (Accenture)

🔬Data Science: Type II Error (Definitions)

"Within a hypothesis test, a type II error is the error of incorrectly not rejecting a null hypothesis when it should be rejected." (Glenn J Myatt, "Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining", 2006)

"A type of error used in hypothesis testing that occurs when the test decision incorrectly “accepts” the null hypothesis. Based on the test statistic, the final decision fails to reject the Null when it is actually false. Type II error also is called 'beta' (β), and the default is typically set at 20%." (Lynne Hambleton, "Treasure Chest of Six Sigma Growth Methods, Tools, and Best Practices", 2007)

"A term that refers to failing to reject a null hypothesis when it is false. It is also sometimes termed a false negative and used when an outcome is incorrectly identified as not having happened, such as when a customer has committed fraud but has not been accurately identified." (Evan Stubbs, "Delivering Business Analytics: Practical Guidelines for Best Practice", 2013)

"Nonrejection of the null hypothesis when it's false." (Geoff Cumming, "Understanding The New Statistics", 2013)

"When the system accepts impostors who should be rejected (false acceptance rate)." (Adam Gordon, "Official (ISC)2 Guide to the CISSP CBK" 4th Ed., 2015)

"Probability of not rejecting the null hypothesis when the null hypothesis is false." (K  N Krishnaswamy et al, "Management Research Methodology: Integration of Principles, Methods and Techniques", 2016)

"Probability of not rejecting the null hypothesis when it's false." (Geoff Cumming, "Understanding The New Statistics", 2013)