1,721,103 research outputs found
Kernel methods
No full textWhat the reader should know to understand this chapter • Notions of calculus. • Chapters 5, 6, and 7. • Although the reading of Appendix D is not mandatory, it represents an advantage for the chapter understanding
Feature extraction methods and manifold learning methods
No full textWhat the reader needs to understand this chapter • Notions of calculus. • The fourth chapter
Foundations of statistical learning and model selection
No full textWhat the reader should know to understand this chapter • Basic notions of machine learning. • Notions of calculus. • Chapter 5
Real-time hand pose recognition
No full textWhat the reader should know to understand this chapter · Color Models (Chap. 3). · Learning Vector Quantization (Chap. 8)
Video segmentation and keyframe extraction
No full textWhat the reader should know to understand this chapter · Basic notions of image processing (Chap. 3). · Clustering techniques (Chap. 6)
Markovian models for sequential data
No full textWhat the reader should know to understand this chapter • Bayes decision theory (Chap. 5). • Lagrange multipliers and conditional optimization problems (Chap. 9). • Probability and statistics (Appendix A)
Automatic personality perception
No full textWhat the reader should know to understand this chapter · Basic notions of speech processing (Chap. 2). · Classification techniques (Chap. 8)
Machine learning
No full textWhat the reader should know after reading in this chapter Supervised learning, Unsupervised learning, Semi-supervised learning, Reinforcement learning
Supervised neural networks and ensemble methods
No full textWhat the reader should know to understand this chapter• Fundamentals of machine learning (Chap. 4)
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