Bioinformatics

Bioinformatics


More Books:

Deep Learning
Language: en
Pages: 800
Authors: Ian Goodfellow, Yoshua Bengio, Aaron Courville
Categories: Computers
Type: BOOK - Published: 2016-11-18 - Publisher: MIT Press

An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO
Introduction to Machine Learning
Language: en
Pages: 584
Authors: Ethem Alpaydin
Categories: Computers
Type: BOOK - Published: 2009-12-04 - Publisher: MIT Press

A new edition of an introductory text in machine learning that gives a unified treatment of machine learning problems and solutions. The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already,
Bioinformatics, second edition
Language: en
Pages: 476
Authors: Pierre Baldi, Soren Brunak
Categories: Computers
Type: BOOK - Published: 2001-07-20 - Publisher: MIT Press

A guide to machine learning approaches and their application to the analysis of biological data. An unprecedented wealth of data is being generated by genome sequencing projects and other experimental efforts to determine the structure and function of biological molecules. The demands and opportunities for interpreting these data are expanding
Convergence and Hybrid Information Technology
Language: en
Pages: 763
Authors: Geuk Lee, Daniel Howard, Jeong Jin Kang, Dominik Slezak
Categories: Computers
Type: BOOK - Published: 2012-08-21 - Publisher: Springer

This book constitutes the refereed proceedings of the 6th International Conference on Convergence and Hybrid Information Technology, ICHIT 2012, held in Daejeon, Korea, in August 2012. The 94 revised full papers presented were carefully reviewed and selected from 196 submissions. The papers are organized in topical sections on communications and
Machine Learning in Non-Stationary Environments
Language: en
Pages: 280
Authors: Masashi Sugiyama, Motoaki Kawanabe
Categories: Computers
Type: BOOK - Published: 2012-03-30 - Publisher: MIT Press

Theory, algorithms, and applications of machine learning techniques to overcome “covariate shift” non-stationarity. As the power of computing has grown over the past few decades, the field of machine learning has advanced rapidly in both theory and practice. Machine learning methods are usually based on the assumption that the data