Machine learning
Machine Learning, a vital and core area of artificial intelligence (AI), is propelling the AI field ever further and making it one of the most compelling areas of computer science research. This textbook offers a comprehensive and unbiased introduction to almost all aspects of machine learning, from...
Saved in:
| Main Authors | , |
|---|---|
| Format | eBook Book |
| Language | English |
| Published |
Singapore
Springer
2021
Springer Singapore |
| Edition | 1 |
| Subjects | |
| Online Access | Get full text |
| ISBN | 9811519668 9789811519666 |
| DOI | 10.1007/978-981-15-1967-3 |
Cover
| Abstract | Machine Learning, a vital and core area of artificial intelligence (AI), is propelling the AI field ever further and making it one of the most compelling areas of computer science research. This textbook offers a comprehensive and unbiased introduction to almost all aspects of machine learning, from the fundamentals to advanced topics. It consists of 16 chapters divided into three parts: Part 1 (Chapters 1-3) introduces the fundamentals of machine learning, including terminology, basic principles, evaluation, and linear models; Part 2 (Chapters 4-10) presents classic and commonly used machine learning methods, such as decision trees, neural networks, support vector machines, Bayesian classifiers, ensemble methods, clustering, dimension reduction and metric learning; Part 3 (Chapters 11-16) introduces some advanced topics, covering feature selection and sparse learning, computational learning theory, semi-supervised learning, probabilistic graphical models, rule learning, and reinforcement learning. Each chapter includes exercises and further reading, so that readers can explore areas of interest. The book can be used as an undergraduate or postgraduate textbook for computer science, computer engineering, electrical engineering, data science, and related majors. It is also a useful reference resource for researchers and practitioners of machine learning. |
|---|---|
| AbstractList | Machine Learning, a vital and core area of artificial intelligence (AI), is propelling the AI field ever further and making it one of the most compelling areas of computer science research. This textbook offers a comprehensive and unbiased introduction to almost all aspects of machine learning, from the fundamentals to advanced topics. It consists of 16 chapters divided into three parts: Part 1 (Chapters 1-3) introduces the fundamentals of machine learning, including terminology, basic principles, evaluation, and linear models; Part 2 (Chapters 4-10) presents classic and commonly used machine learning methods, such as decision trees, neural networks, support vector machines, Bayesian classifiers, ensemble methods, clustering, dimension reduction and metric learning; Part 3 (Chapters 11-16) introduces some advanced topics, covering feature selection and sparse learning, computational learning theory, semi-supervised learning, probabilistic graphical models, rule learning, and reinforcement learning. Each chapter includes exercises and further reading, so that readers can explore areas of interest. The book can be used as an undergraduate or postgraduate textbook for computer science, computer engineering, electrical engineering, data science, and related majors. It is also a useful reference resource for researchers and practitioners of machine learning. |
| Author | Liu, Shaowu Zhou, Zhi-Hua (Computer scientist) |
| Author_xml | – sequence: 1 fullname: Zhou, Zhi-Hua (Computer scientist) – sequence: 2 fullname: Liu, Shaowu |
| BackLink | https://cir.nii.ac.jp/crid/1130008121912460174$$DView record in CiNii |
| BookMark | eNpFkMtLxDAQxiM-0K4LXr15EMRD3UyS5nHUsj5gxYt4DWmaunVLujZV_30TK3qZ4SO_75vMZGjP994hdAr4CjAWCyVkriTkUOSguMjpDsqihiIpvvsvuDxAGRDOeCEEl4doHsIbxpgIwgQhR-jk0dh1691Z58zgW_96jPYb0wU3_-0z9HK7fC7v89XT3UN5vcoNSCrjSGvAxohKOAUVo7WRgjaG1LJuLBM2vlXSKixB1KSxHDtLsDPEGQDLC0Vn6HIKNmHjvsK678agPztX9f0m6Ljh30I0souJDdsh_tENeqIA63SPROuIayh0MujkuJgc26F__3Bh1D_B1vlxMJ1e3pRcYMlUIs8n0rettm2qADTeSAIBBYRxDILRb5jzZmA |
| ContentType | eBook Book |
| Copyright | Springer Nature Singapore Pte Ltd. 2021. Translation from the Chinese Simplified language edition: Machine Learning by Zhi-Hua Zhou, and Shaowu Liu, © Tsinghua University Press 2016. Published by Tsinghua University Press. All Rights Reserved. |
| Copyright_xml | – notice: Springer Nature Singapore Pte Ltd. 2021. Translation from the Chinese Simplified language edition: Machine Learning by Zhi-Hua Zhou, and Shaowu Liu, © Tsinghua University Press 2016. Published by Tsinghua University Press. All Rights Reserved. |
| DBID | RYH |
| DEWEY | 006.31 |
| DOI | 10.1007/978-981-15-1967-3 |
| DatabaseName | CiNii Complete |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISBN | 9811519676 9789811519673 |
| Edition | 1 1st Edition 2021 |
| ExternalDocumentID | 9789811519673 485899 EBC6708493 BC10009054 |
| GroupedDBID | 38. AABBV AABLV ABLLD ABNDO ACBPT ACWLQ AEJLV AEKFX AELOD ALMA_UNASSIGNED_HOLDINGS BAHJK BBABE CZZ DBWEY IEZ OCUHQ ORHYB RYH SBO TPJZQ Z7R Z7U Z7X Z83 Z85 |
| ID | FETCH-LOGICAL-a18387-3ca1c722b7e91b43da873fa2d8dfc47cc72b8c90817d2fc60ec20ea2ea11c6593 |
| ISBN | 9811519668 9789811519666 |
| IngestDate | Mon Jun 02 02:50:21 EDT 2025 Fri May 23 03:30:33 EDT 2025 Fri May 30 21:28:04 EDT 2025 Fri Jun 27 00:11:14 EDT 2025 |
| IsPeerReviewed | false |
| IsScholarly | false |
| LCCallNum_Ident | Q325.5-.7 |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-a18387-3ca1c722b7e91b43da873fa2d8dfc47cc72b8c90817d2fc60ec20ea2ea11c6593 |
| Notes | Translation from Chinese edition (Tsinghua University Press, c2016) Includes bibliographical references and index |
| OCLC | 1264657768 |
| PQID | EBC6708493 |
| PageCount | 460 |
| ParticipantIDs | askewsholts_vlebooks_9789811519673 springer_books_10_1007_978_981_15_1967_3 proquest_ebookcentral_EBC6708493 nii_cinii_1130008121912460174 |
| PublicationCentury | 2000 |
| PublicationDate | c2021 2021 20210821 2021-08-20 |
| PublicationDateYYYYMMDD | 2021-01-01 2021-08-21 2021-08-20 |
| PublicationDate_xml | – year: 2021 text: c2021 |
| PublicationDecade | 2020 |
| PublicationPlace | Singapore |
| PublicationPlace_xml | – name: Singapore |
| PublicationYear | 2021 |
| Publisher | Springer Springer Singapore |
| Publisher_xml | – name: Springer – name: Springer Singapore |
| SSID | ssj0002724722 |
| Score | 2.717329 |
| Snippet | Machine Learning, a vital and core area of artificial intelligence (AI), is propelling the AI field ever further and making it one of the most compelling areas... |
| SourceID | askewsholts springer proquest nii |
| SourceType | Aggregation Database Publisher |
| SubjectTerms | Computer Science Data Mining and Knowledge Discovery Machine Learning Mathematics of Computing |
| TableOfContents | 8.4 Combination Strategies -- 8.5 Diversity -- 8.6 Further Reading -- Exercises -- Break Time -- References -- 9 Clustering -- 9.1 Clustering Problem -- 9.2 Performance Measure -- 9.3 Distance Calculation -- 9.4 Prototype Clustering -- 9.5 Density Clustering -- 9.6 Hierarchical Clustering -- 9.7 Further Reading -- Exercises -- Break Time -- References -- 10 Dimensionality Reduction and Metric Learning -- 10.1 k-Nearest Neighbor Learning -- 10.2 Low-Dimensional Embedding -- 10.3 Principal Component Analysis -- 10.4 Kernelized PCA -- 10.5 Manifold Learning -- 10.6 Metric Learning -- 10.7 Further Reading -- Exercises -- Break Time -- References -- 11 Feature Selection and Sparse Learning -- 11.1 Subset Search and Evaluation -- 11.2 Filter Methods -- 11.3 Wrapper Methods -- 11.4 Embedded Methods and L1 Regularization -- 11.5 Sparse Representation and Dictionary Learning -- 11.6 Compressed Sensing -- 11.7 Further Reading -- Exercises -- Break Time -- References -- 12 Computational Learning Theory -- 12.1 Basic Knowledge -- 12.2 PAC Learning -- 12.3 Finite Hypothesis Space -- 12.4 VC Dimension -- 12.5 Rademacher Complexity -- 12.6 Stability -- 12.7 Further Reading -- Exercises -- Break Time -- References -- 13 Semi-Supervised Learning -- 13.1 Unlabeled Samples -- 13.2 Generative Methods -- 13.3 Semi-Supervised SVM -- 13.4 Graph-Based Semi-Supervised Learning -- 13.5 Disagreement-Based Methods -- 13.6 Semi-Supervised Clustering -- 13.7 Further Reading -- Exercises -- Break Time -- References -- 14 Probabilistic Graphical Models -- 14.1 Hidden Markov Model -- 14.2 Markov Random Field -- 14.3 Conditional Random Field -- 14.4 Learning and Inference -- 14.5 Approximate Inference -- 14.6 Topic Model -- 14.7 Further Reading -- Exercises -- Break Time -- References -- 15 Rule Learning -- 15.1 Basic Concepts -- 15.2 Sequential Covering -- 15.3 Pruning Optimization Intro -- Preface -- Contents -- Symbols -- 1 Introduction -- 1.1 Introduction -- 1.2 Terminology -- 1.3 Hypothesis Space -- 1.4 Inductive Bias -- 1.5 Brief History -- 1.6 Application Status -- 1.7 Further Reading -- Exercises -- Break Time -- References -- 2 Model Selection and Evaluation -- 2.1 Empirical Error and Overfitting -- 2.2 Evaluation Methods -- 2.3 Performance Measure -- 2.4 Comparison Test -- 2.5 Bias and Variance -- 2.6 Further Reading -- Exercises -- Break Time -- References -- 3 Linear Models -- 3.1 Basic Form -- 3.2 Linear Regression -- 3.3 Logistic Regression -- 3.4 Linear Discriminant Analysis -- 3.5 Multiclass Classification -- 3.6 Class Imbalance Problem -- 3.7 Further Reading -- Exercises -- Break Time -- References -- 4 Decision Trees -- 4.1 Basic Process -- 4.2 Split Selection -- 4.3 Pruning -- 4.4 Continuous and Missing Values -- 4.5 Multivariate Decision Trees -- 4.6 Further Reading -- Exercises -- Break Time -- References -- 5 Neural Networks -- 5.1 Neuron Model -- 5.2 Perceptron and Multi-layer Network -- 5.3 Error Backpropagation Algorithm -- 5.4 Global Minimum and Local Minimum -- 5.5 Other Common Neural Networks -- 5.6 Deep Learning -- 5.7 Further Reading -- Exercises -- Break Time -- References -- 6 Support Vector Machine -- 6.1 Margin and Support Vector -- 6.2 Dual Problem -- 6.3 Kernel Function -- 6.4 Soft Margin and Regularization -- 6.5 Support Vector Regression -- 6.6 Kernel Methods -- 6.7 Further Reading -- Exercises -- Break Time -- References -- 7 Bayes Classifiers -- 7.1 Bayesian Decision Theory -- 7.2 Maximum Likelihood Estimation -- 7.3 Naïve Bayes Classifier -- 7.4 Semi-Naïve Bayes Classifier -- 7.5 Bayesian Network -- 7.6 EM Algorithm -- 7.7 Further Reading -- Exercises -- Break Time -- References -- 8 Ensemble Learning -- 8.1 Individual and Ensemble -- 8.2 Boosting -- 8.3 Bagging and Random Forest 15.4 First-Order Rule Learning -- 15.5 Inductive Logic Programming -- 15.6 Further Reading -- Exercises -- Break Time -- References -- 16 Reinforcement Learning -- 16.1 Task and Reward -- 16.2 K-Armed Bandit -- 16.3 Model-Based Learning -- 16.4 Model-Free Learning -- 16.5 Value Function Approximation -- 16.6 Imitation Learning -- 16.7 Further Reading -- Exercises -- Break Time -- References -- Appendix A Matrix -- A.1 Basic Operations -- A.2 Derivative -- A.3 Singular Value Decomposition -- Appendix B Optimization -- B.1 Lagrange Multiplier Method -- B.2 Quadratic Programming -- B.3 Semidefinite Programming -- B.4 Gradient Descent Method -- B.5 Coordinate Descent Method -- Appendix C Probability Distributions -- C.1 Common Probability Distributions -- C.2 Conjugate Distribution -- C.3 Kullback-Leibler Divergence -- Index |
| Title | Machine learning |
| URI | https://cir.nii.ac.jp/crid/1130008121912460174 https://ebookcentral.proquest.com/lib/[SITE_ID]/detail.action?docID=6708493 http://link.springer.com/10.1007/978-981-15-1967-3 https://www.vlebooks.com/vleweb/product/openreader?id=none&isbn=9789811519673 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3db9MwELdYeWEv41MUGKoQD7wYxU5sx4-sKqqmwQtj2ptlOw6LmFqJtiDx1_Nz6iRtQULwYiW245PvnLvznc9HyGuVVa4QmlMJFkkLbjV1ITBay7oUrnJB2Bgo_OGjnH8uzq_F9ZA3rI0uWbu3_ucf40r-h6qoA11jlOw_ULYfFBV4Bn1RgsIoD5Tf_jXlXmqPQIYu58OXwfy73LTuhpuGzje2ddGmtA0p9LGJMR795v-iabt_urHLH5tdAwBnBwaAzgC4tzHUJTQ9_FzbjCa_scnhZAQ6UiYouoLXDDKhP6l3No0OAA3F7ogcKZWNyN13s_OLq96OxRWP901u7zIawGID3L-UnTc5Xei7B_OYHNvVVzB0MPv1ChJ-0TR72v6Bg7qV-5f3ySjGgjwgd8LiITnpUDlJDPEROUmEmHSEeEyu3s8up3OackxQC2YWGay3zGMCTgXNXJFXtlR5bXlVVrUvlEebK72G5qQqXnuZBc-zYHmwjHkpdP6EjBbLRXhKJlxVlUCjL60svJPas8wLD4009zpnckxe7UzVfL9t_eErs4M4lY_JKTBgfBNLFp2NAA2xAj0Me2dVjMmkw41pv0-HeM3sbCpVVhYaQ7zpcGa2ELq7qQHJAJRhwkRgJn_2F2jPyb1hyb0go_W3TTiFFrZ2L9NC-AVH-CGs |
| linkProvider | Library Specific Holdings |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=book&rft.title=Machine+learning&rft.au=Zhou%2C+Zhi-Hua+%28Computer+scientist%29&rft.au=Liu%2C+Shaowu&rft.date=2021-01-01&rft.pub=Springer&rft.isbn=9789811519666&rft_id=info:doi/10.1007%2F978-981-15-1967-3&rft.externalDocID=BC10009054 |
| thumbnail_m | http://utb.summon.serialssolutions.com/2.0.0/image/custom?url=https%3A%2F%2Fvle.dmmserver.com%2Fmedia%2F640%2F97898115%2F9789811519673.jpg |
| thumbnail_s | http://utb.summon.serialssolutions.com/2.0.0/image/custom?url=https%3A%2F%2Fmedia.springernature.com%2Fw306%2Fspringer-static%2Fcover-hires%2Fbook%2F978-981-15-1967-3 |