Machine Learning Books

This is deja vu again. We talked about AI and robotics in the same vein. Definitely ground breaking and recommend it. Not sure novices can handle it...

The book is one of the best I have seen in Neural and computer intelligence. Highly recommend if you are a student, academia, computer R&D or hobbyist.

Wish somebody published this before I started following Neural networks or computer intelligence

The volume brings an outstanding presentation of the major issues, ideas, concepts and algorithms to design and develop fuzzy systems using gentic algorithms. A field of major relevance for researchers and practioners, genetic fuzzy systems provides a major methodological substract of significant impact in practice. The book is unique in its contents and presentation. Chapters begin with the key concepts and smoothly grows to advanced concepts in a clear and very understandable and motivating way. The material mirrors the state of the art in the area of genetic fuzzy systems and contains the most recent results available until its publication. Written by renowned, internationally recognized researchers, the book is mandatory to all who are interested in the field of computational intelligence, its foundations and applications.

In recent years, a great number of publications have explored the use of genetic algorithms as a tool for designing fuzzy
systems. Genetic Fuzzy Systems explores and discusses this symbiosis of evolutionary computation and fuzzy logic. The book summarizes and analyzes the novel field of genetic fuzzy systems, paying special attention to genetic algorithms that adapt and learn
the knowledge base of a fuzzy-rule-based system. It introduces the general concepts, foundations and design principles of genetic fuzzy
systems and covers the topic of genetic tuning of fuzzy systems. It also introduces the three fundamental approaches to genetic learning
processes in fuzzy systems: the Michigan, Pittsburgh and Iterative-learning methods. Finally, it explores hybrid genetic fuzzy systems such as
genetic fuzzy clustering or genetic neuro-fuzzy systems and describes a number of applications from different areas. Genetic Fuzzy System represents a comprehensive treatise on the design of the fuzzy-rule-based systems using genetic algorithms, both from
a theoretical and a practical perspective. It is a valuable compendium for scientists and engineers concerned with research and applications in
the domain of fuzzy systems and genetic algorithms.

My 4 and 5 year olds have enjoyed reading this several times already! It's a good way to introduce simple machines to this age group, and has colorful pictures too! It's fun to think of lifting lions, or moving pandas on a wagon, or getting a bunch of bananas to a baboon party!

This book easily and clearly illustrates the concept of pulley, screw and lever. Simple machines have never been so easy to explain. The cute animals and the banana party they have help the students in my 3rd grade class to understand force and work. Bravo!

Well written and clear material.
As a researcher in dynamical systems, I found this book a very interesting introduction to neural nets.
Would like to see the section on genetic programming fleshed out even further.

DESEAMOS OBTENER MAS INFORMACION SOBRE EL TEMA, SOMOS UNA INSTITUCION EDUCATIVA ESPECIALIZADA EN EL CAMPO DE LA CRIMINALISTICA.

AGRADECEMOS LA COLABORACION PRESTADA, SI ES POSIBLE NOS GUSTARIA UN DEMO.

It's a collection of fifty years of research in the area of inductive computer-aided algorithms, machine learning and advanced neural networks. The practical examples given in the book are superb. We have used the same concept in our data mining and pattern recognition works. The scripts given in the book with slight modification are very useful in building the networks. I suggest anybody in the specialization of neural networks, machine intelligence and data mining to use this abundent knowledge in their practical works.

The book "Inductive Learning Algorithms for Complex System Modeling" by Hema Madala and Alexei Ivakhnenko decsribes the learning algorithms in the first few chapters. First chapter gives historical perspective of complex nature of the subject. The next few chapters explain the importance of selection criteria and various computing algorithms.

These algorithms are similar to the artificial neural network algorithms which are being used widely in analysis, identification and predicting the fuzzy complex systems; only difference is that they are inductive in nature. They have different strategy in their forecasting approach.

The authors have dealt with the data from some real life complex systems and presented their results. They have given some comparitive results with the functioning of neural networks. Finally the actual source code has been supplied. This is still a valid code and one can convert it to utilize for modern data mining applications which come under the arena of internet CRM applications.

Though this has been first published in 1994, these algorithms are still valid for the modern usage. It would be worthy to consider for publishing a second edition by adding some latest contribution on the topic from the authors.

There is no other book available with such a valuable elaborative information on the topic.

This is a must read for anyone beginning to investigate the analysis of meaning in text using computational methods. I found the initial sections were useful in bringing together my thought on many different aspects of the topic.

This is a Tesis Work, it contains a review and conmparation of several learners. It focuses mainly on SVM.

For years I have looked for a credible book that traced the lineage of human and organizational performance improvement. All others had biases and lapses. This book written by one of the Masters who helped define and develop this field has written the best account of our lineage I have ever seen. It is beautifully written--clear, concise, accurate, and human--and meets (no, exceeds) my expectations.

It is a must for any student (senior or starting) in our field.

Although I selected Dr. Mager from all of the experts in the early 1960's to work with a major management consulting firm to introduce programmed instruction into European countries, and have stayed in touch since then, I learned more about him and his genius as I turned each page of this book. It is essential reading for everyone in the fields of education, training and management.

A comprehensive guide concerned with understanding basics, modeling, analyzing Neuro-Fuzzy Networks. The examples and the illustraions are clear with a lot of Matlab codes. I recommend this book.

The book provides a good overview to a wide disciplines of knowledge including fuzzy sets, neural nets, genetic algorithms and their composite use for developing high performance intelligent systems.The principles are explained with many examples and illustrations. The book is highly readable for its simplicity in presentation style. It is useful to anyone interested in this broad discipline.

This is a seminal work in the field of Artificial Intelligence. Following an initial period of enthusiasm, the field encountered a period of frustration and disrepute. Minksy and Papert's 1969 book summed up this general feeling of frustration among researchers by demonstrating the representational limitations of Perceptrons (used in neural networks). Their arguments were very influential in the field and accepted by most without further analysis.

I found this book to be generally easy to read. Despite being written in 1969, it is still very timely.

In 1958, Cornell psychologist Frank Rosenblatt proposed the 'perceptron', one of the first neural networks to become widely known. A retina sensory layer projected to an association layer made up of threshold logic units which in turn connected to the third layer, the response layer. If two groups of patterns are linearly separable then the perceptron network works well in learning to classify them in separate classes. In this reference, Minsky and Papert show that assuming a diameter-limited sensory retina, a perceptron network could not always compute connectedness, ie, determining if a line figure is one connected line or two separate lines. Extrapolating the conclusions of this reference to other sorts of neural networks was a big setback to the field at the time of this reference. However, it was subsequently shown that having an additional 'hidden' layer in the neural network overcame many of the limitations. This reference figures so prominently in the field of neural networks, and is often referred to in modern works. But of even greater significance, the history of the perceptron demonstrates the complexity of analyzing neural networks. Before this reference, artificial neural networks were considered terrific, after this reference limited, and then in the 1980s terrific again. But at the time of this writing, it is realized that despite physiological plausibility, artificial neural networks do not scale well to large or complex problems that brains can easily handle, and artificial neural networks as we know them may actually be not so terrific.

I give this book 5 stars. It is a must have book, very well written. It has good balance between rigorous theory and authors reasonong regarding the subject.
I'm not a beginner in this field, and still I found a lot of interesting ideas, that can help not only to improve quality of the net, but also make you see "bigger picture".

"Recurrent Neural Networks for Prediction: Learning Algorithms,
Architectures and Stability," approaches the field of recurrent neural networks from both a practical and a theoretical perspective. Starting from the fundamentals, where unexpected insights are offered even at the level of the dynamical richness of simple neurons, the authors describe many existing algorithms and gradually introduce novel ones. The latter are convicingly shown to yield better prediction performances than traditional approaches, when applied to real-world data. They also dedicate a considerable amount of time on the (practical) issue of nonlinearity analysis of time series, which is or should be, indeed, the cradle of all proper modelling and/or filtering solutions: nonlinearity should be assessed prior to choosing the appropriate model and/or filters, since linear ones are to be preferred if sufficient for the problem. I would recommend this book to any researcher who is active in the field of recurrent neural networks and time series analysis, but also to researchers who are new in the field, since the book offers an extensive overview of the current state-of-the-art approaches.