Machine Learning: A Guide to Current Research
by Tom M. Mitchell
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Description
One of the currently most active research areas within Artificial Intelligence is the field of Machine Learning. which involves the study and development of computational models of learning processes. A major goal of research in this field is to build computers capable of improving their performance with practice and of acquiring knowledge on their own. The intent of this book is to provide a snapshot of this field through a broad. representative set of easily assimilated short papers. As show more such. this book is intended to complement the two volumes of Machine Learning: An Artificial Intelligence Approach (Morgan-Kaufman Publishers). which provide a smaller number of in-depth research papers. Each of the 77 papers in the present book summarizes a current research effort. and provides references to longer expositions appearing elsewhere. These papers cover a broad range of topics. including research on analogy. conceptual clustering. explanation-based generalization. incremental learning. inductive inference. learning apprentice systems. machine discovery. theoretical models of learning. and applications of machine learning methods. A subject index IS provided to assist in locating research related to specific topics. The majority of these papers were collected from the participants at the Third International Machine Learning Workshop. held June 24-26. 1985 at Skytop Lodge. Skytop. Pennsylvania. While the list of research projects covered is not exhaustive. we believe that it provides a representative sampling of the best ongoing work in the field. and a unique perspective on where the field is and where it is headed. show lessTags
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Member Reviews
This is an introductory book on Machine Learning. There is quite a lot of mathematics and statistics in the book, which I like. A large number of methods and algorithms are introduced:
Neural Networks
Bayesian Learning
Genetic Algorithms
Reinforcement Learning
The material covered is very interesting and clearly explained. I find the presentation, however, a bit lacking. I think it has to do with the chosen fonts and lack of highlighting of important terms. Maybe it would have been better to have shorter paragraphs too.
If you are looking for an introductory book on machine learning right now, I would not recommend this book, because in recent years better books have been written on the subject. These are better obviously, because they show more include coverage of more modern techniques. I give this book 3 out of 5 stars. show less
Neural Networks
Bayesian Learning
Genetic Algorithms
Reinforcement Learning
The material covered is very interesting and clearly explained. I find the presentation, however, a bit lacking. I think it has to do with the chosen fonts and lack of highlighting of important terms. Maybe it would have been better to have shorter paragraphs too.
If you are looking for an introductory book on machine learning right now, I would not recommend this book, because in recent years better books have been written on the subject. These are better obviously, because they show more include coverage of more modern techniques. I give this book 3 out of 5 stars. show less
There is no math in this book. Why is there no math in this book!?
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Common Knowledge
- Canonical title
- Machine Learning: A Guide to Current Research
Classifications
- Genres
- Technology, Nonfiction, General Nonfiction
- DDC/MDS
- 006.31 — Computer science, information & general works Computer science, knowledge & systems Special computer methods (AI, barcoding, VR, web design, social media) Artificial Intelligence Machine Learning
- LCC
- Q325.5 .M58 — Science Science (General) Cybernetics
- BISAC
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- Members
- 359
- Popularity
- 87,239
- Reviews
- 4
- Rating
- (3.92)
- Languages
- English, French, German
- Media
- Paper, Ebook
- ISBNs
- 6
- ASINs
- 1
