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Encyclopedia of Machine Learning and Data Science [electronic resource] /

Contributor(s): Phung, Dinh [editor.] | Webb, Geoffrey I [editor.] | Sammut, Claude [editor.] | SpringerLink (Online service).
Material type: materialTypeLabelBookPublisher: New York, NY : Springer US : Imprint: Springer, 2019Description: XXV, 1975 p. 600 illus. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9781489975027.Subject(s): Computer Science | Artificial intelligence | Data mining | Mathematical statistics | Optical pattern recognition | Artificial Intelligence | Data Mining and Knowledge Discovery | Statistics and Computing/Statistics Programs | Pattern RecognitionOnline resources: Click here to access online
Contents:
Abduction -- Adaptive Resonance Theory -- Anomaly Detection -- Bayes Rule -- Case-Based Reasoning -- Categorical Data Clustering -- Causality -- Clustering from Data Streams -- Complexity in Adaptive Systems -- Complexity of Inductive Inference -- Computational Complexity of Learning -- Confusion Matrix -- Connections Between Inductive Inference and Machine Learning -- Covariance Matrix -- Decision List -- Decision Lists and Decision Trees -- Decision Tree -- Deep Learning -- Density-Based Clustering -- Dimensionality Reduction -- Document Classification -- Dynamic Memory Model -- Empirical Risk Minimization -- Error Rate -- Event Extraction from Media Texts -- Evolutionary Clustering -- Evolutionary Computation in Economics -- Evolutionary Computation in Finance -- Evolutionary Computational Techniques in Marketing -- Evolutionary Feature Selection and Construction -- Evolutionary Kernel Learning -- Evolutionary Robotics -- Expectation Maximization Clustering -- Expectation Propagation -- Feature Construction in Text Mining -- Feature Selection -- Feature Selection in Text Mining -- Gaussian Distribution -- Gaussian Process -- Generative and Discriminative Learning -- Grammatical Inference -- Graphical Models -- Hidden Markov Models -- Inductive Inference -- Inductive Logic Programming -- Inductive Programming -- Inductive Transfer -- Inverse Reinforcement Learning -- Kernel Methods -- K-Means Clustering -- K-Medoids Clustering -- K-Way Spectral Clustering -- Learning Algorithm Evaluation -- Learning Graphical Models -- Learning Models of Biological Sequences -- Learning to Rank -- Learning Using Privileged Information -- Linear Discriminant -- Linear Regression -- Locally Weighted Regression for Control -- Machine Learning and Game Playing -- Manhattan Distance -- Maximum Entropy Models for Natural Language Processing -- Mean Shift -- Metalearning -- Minimum Description Length Principle -- Minimum Message Length -- Mixture Model -- Model Evaluation -- Model Trees -- Multi Label Learning -- Naïve Bayes -- Occam's Razor -- Online Controlled Experiments and A/B Testing -- Online Learning -- Opinion Stream Mining -- PAC Learning -- Partitional Clustering -- Phase Transitions in Machine Learning.
In: Springer Nature Living ReferenceSummary: This authoritative, expanded and updated third edition of Encyclopedia of Machine Learning and Data Mining provides easy access to core information for those seeking entry into any aspect within the broad field of Machine Learning and Data Mining. A paramount work, its 1000 entries – over 200 of them newly updated or added --are filled with valuable literature references, providing the reader with a portal to more detailed information on any given topic.Topics for the Encyclopedia of Machine Learning and Data Science include recent developments in Deep Learning, Learning and Logic, Data Mining, Applications, Text Mining, Statistical Learning, Reinforcement Learning, Pattern Mining, Graph Mining, Relational Mining, Evolutionary Computation, Information Theory, Behavior Cloning, and many others. Topics were selected by a distinguished international advisory board. Each peer-reviewed, highly-structured entry includes a definition, key words, an illustration, applications, a bibliography, and links to related literature.The entries are expository and tutorial, making this reference a practical resource for students, academics, or professionals who employ machine learning and data mining methods in their projects. Machine learning and data mining techniques have countless applications, including data science applications, and this reference is essential for anyone seeking quick access to vital information on the topic.
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Abduction -- Adaptive Resonance Theory -- Anomaly Detection -- Bayes Rule -- Case-Based Reasoning -- Categorical Data Clustering -- Causality -- Clustering from Data Streams -- Complexity in Adaptive Systems -- Complexity of Inductive Inference -- Computational Complexity of Learning -- Confusion Matrix -- Connections Between Inductive Inference and Machine Learning -- Covariance Matrix -- Decision List -- Decision Lists and Decision Trees -- Decision Tree -- Deep Learning -- Density-Based Clustering -- Dimensionality Reduction -- Document Classification -- Dynamic Memory Model -- Empirical Risk Minimization -- Error Rate -- Event Extraction from Media Texts -- Evolutionary Clustering -- Evolutionary Computation in Economics -- Evolutionary Computation in Finance -- Evolutionary Computational Techniques in Marketing -- Evolutionary Feature Selection and Construction -- Evolutionary Kernel Learning -- Evolutionary Robotics -- Expectation Maximization Clustering -- Expectation Propagation -- Feature Construction in Text Mining -- Feature Selection -- Feature Selection in Text Mining -- Gaussian Distribution -- Gaussian Process -- Generative and Discriminative Learning -- Grammatical Inference -- Graphical Models -- Hidden Markov Models -- Inductive Inference -- Inductive Logic Programming -- Inductive Programming -- Inductive Transfer -- Inverse Reinforcement Learning -- Kernel Methods -- K-Means Clustering -- K-Medoids Clustering -- K-Way Spectral Clustering -- Learning Algorithm Evaluation -- Learning Graphical Models -- Learning Models of Biological Sequences -- Learning to Rank -- Learning Using Privileged Information -- Linear Discriminant -- Linear Regression -- Locally Weighted Regression for Control -- Machine Learning and Game Playing -- Manhattan Distance -- Maximum Entropy Models for Natural Language Processing -- Mean Shift -- Metalearning -- Minimum Description Length Principle -- Minimum Message Length -- Mixture Model -- Model Evaluation -- Model Trees -- Multi Label Learning -- Naïve Bayes -- Occam's Razor -- Online Controlled Experiments and A/B Testing -- Online Learning -- Opinion Stream Mining -- PAC Learning -- Partitional Clustering -- Phase Transitions in Machine Learning.

This authoritative, expanded and updated third edition of Encyclopedia of Machine Learning and Data Mining provides easy access to core information for those seeking entry into any aspect within the broad field of Machine Learning and Data Mining. A paramount work, its 1000 entries – over 200 of them newly updated or added --are filled with valuable literature references, providing the reader with a portal to more detailed information on any given topic.Topics for the Encyclopedia of Machine Learning and Data Science include recent developments in Deep Learning, Learning and Logic, Data Mining, Applications, Text Mining, Statistical Learning, Reinforcement Learning, Pattern Mining, Graph Mining, Relational Mining, Evolutionary Computation, Information Theory, Behavior Cloning, and many others. Topics were selected by a distinguished international advisory board. Each peer-reviewed, highly-structured entry includes a definition, key words, an illustration, applications, a bibliography, and links to related literature.The entries are expository and tutorial, making this reference a practical resource for students, academics, or professionals who employ machine learning and data mining methods in their projects. Machine learning and data mining techniques have countless applications, including data science applications, and this reference is essential for anyone seeking quick access to vital information on the topic.

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