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4 results for "neural network software"
Practical Artificial Intelligence Programming With Java By Mark Watson
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This book uses both best of breed open source software and the author's own libraries to introduce the reader to Artificial Intelligence (AI) technologies like genetic algorithms, neural networks,... More > expert systems, machine learning, and statistical natural language processing (NLP).< Less
Practical Artificial Intelligence Programming With Java By Mark Watson
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This book uses both best of breed open source software and the author's own libraries to introduce the reader to Artificial Intelligence (AI) technologies like genetic algorithms, neural networks,... More > expert systems, machine learning, and statistical natural language processing (NLP).< Less
Predictive Modeling and Analytics By Jeffrey Strickland
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This book is about predictive modeling. Yet, each chapter could easily be handled by an entire volume of its own. So one might think of this as a survey of predictive models, both statistical and... More > machine learning. We define predictive modeling as a statistical model or machine learning model used to predict future behavior based on past behavior. In order to use this book, the reader should have a basic understanding of statistics (statistical inference, models, tests, etc.)—this is an advanced book. Every chapter culminates in an example using R. R is a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS. The book is organized so that statistical models are presented first (hopefully in a logical order), followed by machine learning models, and then applications: uplift modeling and time series. One could use this as a textbook with problem solving in R (there are no “by-hand” exercises).< Less
Introduction to Statistical Tools Used for Data Mining By Jim Rutledge, Ph.D.
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Many projects can be very data intensive and the statistical tools taught in traditional college curricula are often inadequate for solving the problem at hand. While there are many aspects and... More > definitions of data mining, the basic idea is to find patterns in data sets. Although marketed for large databases, the techniques are applicable to any size data set. While data management is an important subject, this course will focus on the statistical models used in data mining. It will cover the most useful tools such as linear regression, logistic regression, classification trees, discriminate analysis, neural networks, clustering, nearest neighbors, bump hunting, and market basket analysis. While these data mining tools may sound exotic and difficult to understand, they are actually fairly straightforward and modern software makes it easy to use them. This is a color version for AAA.< Less