Pathways to Machine Learning and Soft Computing: . 2283;?????

Jyh-Horng Jeng; ???

ISBN 10: 1647848601 / ISBN 13: 9781647848606

Editore: Ehgbooks, 2018

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Dati bibliografici

• Titolo: Pathways to Machine Learning and Soft Computing: . 2283;?????
• Editore: Ehgbooks
• Data di pubblicazione: 2018
• Lingua: Inglese
• ISBN 10: 1647848601
• ISBN 13: 9781647848606
• Rilegatura: Brossura
• Condizione: New

Descrizione articolo

Riassunto

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.

本書將介紹常用的機器學習(machine learning)方法以及軟計算(soft computing)如演化計算(evolutionary computation)等。主要的主題包括人工神經網路(Artificial Neural Network, ANN)、徑向函數網路 (Radial Basis Function Network, RBFN)、模糊神經網路 (Fuzzy Neural Network, FNN)、支撐向量機 (Support Vector Machine, SVM) 以及 Wilcoxon 學習機 (Wilcoxon Learning Machine, WLM)等。軟計算方面的主題則包括基因演算法 (Genetic Algorithm, GA) 和粒子群聚最佳化 (Particle Swarm Optimization, PSO)。

本書的重點是機器學習的基礎,包括模型的建立以及學習演算法的的推導。同時也提供許多的範例、圖示、表格、習題與解答。此外,針對所有演算法,本書提供使用 R 程式的實現和驗證,這些 R 程式都使用模擬資料驗證成功,讀者可以很容易使用其他的程式語言來實現,並且可以跟本書所附帶的 R 程式碼交叉驗證。讀完此書必然功力大增。

本書最重要的特色就是提供所有演算法的 Pre-Pseudo Code,也就是說,使用類似程式語言 Pseudo Code 方式,將數學公式以步驟的方式表達出來,非常簡易而且清楚,任何學生或工程師在還不完全了解演算法的情形之下,就可以根據所提供的 Pre-Pseudo Code 使用各種程式語言來實現;另一方面,讀者也可藉由這樣的實現方式來理解演算法的推導。