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03475nam a22003495i 4500 |
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20210624114710.0 |
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cr nn 008mamaa |
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170413s2017 xxu| s |||| 0|eng d |
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|a 9781489976871
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| 024 |
7 |
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|a 10.1007/978-1-4899-7687-1
|2 doi
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| 040 |
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|a Sistema de Bibliotecas del Tecnológico de Costa Rica
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| 245 |
1 |
0 |
|a Encyclopedia of Machine Learning and Data Mining /
|c edited by Claude Sammut, Geoffrey I. Webb.
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| 250 |
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|a 2nd ed. 2017.
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| 260 |
# |
# |
|a New York, NY :
|b Springer US :
|b Imprint: Springer,
|c 2017.
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| 300 |
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|a 263 illus., 83 illus. in color. eReference. :
|b online resource.
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| 336 |
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 505 |
0 |
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|a 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.
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| 650 |
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0 |
|a Artificial intelligence.
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| 650 |
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0 |
|a Data mining.
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| 650 |
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0 |
|a Statistics .
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| 650 |
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0 |
|a Pattern recognition.
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| 650 |
1 |
4 |
|a Artificial Intelligence.
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| 650 |
2 |
4 |
|a Data Mining and Knowledge Discovery.
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| 650 |
2 |
4 |
|a Statistics and Computing/Statistics Programs.
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| 650 |
2 |
4 |
|a Pattern Recognition.
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| 700 |
1 |
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|a Sammut, Claude.
|e editor.
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| 700 |
1 |
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|a Webb, Geoffrey I.
|e editor.
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| 710 |
2 |
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|a SpringerLink (Online service)
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| 773 |
0 |
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|t Springer Nature eReference
|