Machine Learning and Visual Perception
Together with Tsinghua University Press
- A systematic presentation of machine learning and visual perception, covering both classical and recent results.
- Includes extensive examples to facilitate understanding.
- Presents the advanced theories in an easy-to-understand way.
Aims and Scope
The book provides an up-to-date overview on machine learning and visual perception, including decision tree, Bayesian learning, support vector machine, AdaBoost, object detection, compressive sensing, deep learning, and reinforcement learning. Both classic and novel algorithms are introduced. With abundant practical examples, it is an essential reference to students, lecturers, professionals and any interested lay readers in computer science.
- Approx. x, 190 pages
- 100 Fig. 50 Tables
- Type of Publication:
- Professional Book