Principles of Machine Learning The Three Perspectives /
Conducting an in-depth analysis of machine learning, this book proposes three perspectives for studying machine learning: the learning frameworks, learning paradigms, and learning tasks. With this categorization, the learning frameworks reside within the theoretical perspective, the learning paradig...
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| 第一著者: | |
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| 団体著者: | |
| フォーマット: | 電子媒体 eBook |
| 言語: | 英語 |
| 出版事項: |
Singapore :
Springer Nature Singapore : Imprint: Springer,
2025.
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| 版: | 1st ed. 2025. |
| 主題: | |
| オンライン･アクセス: | https://doi.org/10.1007/978-981-97-5333-8 |
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目次:
- Part I. Perspectives
- Chapter 1. Introduction
- Chapter 2. On Perspectives
- Part II. Frameworks. Chapter 3. Probabilistic Framework
- Chapter 4. Statistical Framework
- Chapter 5. Connectionist Framework
- Chapter 6. Symbolic Framework
- Chapter 7. Behavioral Framework
- Part III. Paradigms
- Chapter 8. Supervised Learning Paradigm
- Chapter 9. Unsupervised Learning Paradigm
- Chapter 10. Reinforcement Learning Paradigm
- Chapter 11. Other Learning Quasi-Paradigm
- Part IV. Tasks
- Chapter 12. Classification Task
- Chapter 13. Regression Task
- Chapter 14. Clustering Task
- Chapter 15. Dimensionality Reduction Task
- Appendices.