邁向機器學習與軟計算之路(國際英文版)
Pathways to Machine Learning and Soft Computing
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內容簡介
This book provides frequently studied and used machines together with soft computing methods such as evolutionary computation. The main topics of the machine learning cover Artificial Neural Networks (ANNs), Radial Basis Function Networks (RBFNs), Fuzzy Neural Networks (FNNs), Support Vector Machines (SVMs), and Wilcoxon Learning Machines (WLMs). The soft computing methods include Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The contents are basics of machine learning, including construction of models and derivation of learning algorithms. This book also provides lots of examples, figures, illustrations, tables, exercises, and the solution menu. In addition, the simulated and validated codes written in R are also provided for the user to learn the programming procedure when written in different programming languages. The R codes work correctly on many simulated datasets. So, the readers can verify their own codes by comparison. Reading this book will become strong. One most important feature of this book is that we provide step by step illustrations for every algorithm, which is referred to as pre-pseudo codes. The pre-pseudo codes arrange complicated algorithms in the forms of mathematical equations, which are ready for programming using any languages. It means that students and engineers can easily implement the algorithms from the pre-pseudo codes even they do not fully understand the underlying ideas. On the other hand, implementing the pre-pseudo codes will help them to understand the ideas.
目錄
About Author
About the Book
Table of Contents
Brief Sketch of the Contents
Chapter 1 Introduction
Chapter 2 Finite-dimensional Optimization
Chapter 3 Linear Classification
Chapter 4 Linear Regression
Chapter 5 Multi-layer Neural Networks
Chapter 6 Kernel-based Support Vector Classification and Regression
Chapter 7 Sequential Minimal Optimization Techniques
Chapter 8 Model Selection
Chapter 9 Wilcoxon Learning Machines
About the Book
Table of Contents
Brief Sketch of the Contents
Chapter 1 Introduction
Chapter 2 Finite-dimensional Optimization
Chapter 3 Linear Classification
Chapter 4 Linear Regression
Chapter 5 Multi-layer Neural Networks
Chapter 6 Kernel-based Support Vector Classification and Regression
Chapter 7 Sequential Minimal Optimization Techniques
Chapter 8 Model Selection
Chapter 9 Wilcoxon Learning Machines
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