685 results for machine learning

Machine learning

Hendrik Blockeel; Dragan Gamberger

Springer · 2003 · English · Open Library

Bayesian machine learning

Sounak Chakraborty

Gainesville, FL · 2005 · English · Open Library

Intelligent scheduling with machine learning capabilities

Michael Shaw

College of Commerce and Business Administration, University of Illinois at… · 1990 · English · Open Library

Machine Learning, ECML 2004: 15th European Conference on Machine Learning, Pisa, Italy, September 20-24, 2004, Proceedings

Boulicaut, Jean-François; Esposito, F; Giannotti, F et al.

Springer · 2004 · English · HAL

International audience

Incorporating machine learning in knowledge-based process planning systems

Michael Shaw

College of Commerce and Business Administration, University of Illinois at… · 1990 · English · Open Library

A methodology for software cost estimation using machine learning techniques

Michael A. Kelly

Naval Postgraduate School · 1993 · English · Open Library

A machine learning approach to following trace particles in turbulent flow

Randel Allan Crowe

1977 · English · Open Library

Machine Learning for Detecting Gene-Gene Interactions

McKinney, Brett A.; Reif, David M.; Ritchie, Marylyn D. et al.

Appl Bioinformatics · 2006 · English · PubMed Central

…occur between more than two polymorphisms. In this review, we discuss machine-learning models and algorithms for identifying and characterising susceptibility genes in common, complex, multifactorial human diseases. We focus on the following machine-learning methods that have been used to detect…

Application of machine learning in SNP discovery

Matukumalli, Lakshmi K; Grefenstette, John J; Hyten, David L et al.

BMC Bioinformatics · 2006 · English · PubMed Central

…SNP predictions even with stringent parameter values. We developed a machine learning (ML) method to augment PolyBayes to improve its prediction accuracy. ML methods have also been successfully applied to other bioinformatics problems in predicting genes, promoters, transcription factor binding…

Also available at: DOAJ

Gaussian Processes for Machine Learning

Rasmussen, Carl Edward; Williams, Christopher K. I.

The MIT Press · 2005 · English · DOAB

A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines. Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received…

An integrated framework for applying machine learning in intelligent decision support systems

Michael Shaw

College of Commerce and Business Administration, University of Illinois at… · 1988 · English · Open Library

Clinical Data Processing Tools: A Machine Learning Resource

Ohno-Machado, Lucila; Vinterbo, Staal; Øhrn, Aleksander et al.

Proc AMIA Symp · 1999 · English · PubMed Central

A Protein Classification Benchmark collection for machine learning

Sonego, Paolo; Pacurar, Mircea; Dhir, Somdutta et al.

Nucleic Acids Res · 2006 · English · PubMed Central

Protein classification by machine learning algorithms is now widely used in structural and functional annotation of proteins. The Protein Classification Benchmark collection () was created in order to provide standard datasets on which the performance of machine learning methods can be compared. It…

Advancing Post-Genome Data and System Integration Through Machine Learning

Azuaje, Francisco

Comp Funct Genomics · 2002 · English · PubMed Central

Research on biological data integration has traditionally focused on the development of systems for the maintenance and interconnection of databases. In the next few years, public and private biotechnology organisations will expand their actions to promote the creation of a post-genome semantic…

Filtering for medical news items using a machine learning approach.

Zheng, Wanhong; Milios, Evangelos; Watters, Carolyn

Proc AMIA Symp · 2002 · English · PubMed Central

…domain. This paper addresses the medical news-filtering problem using a machine learning approach. We describe the application of two supervised machine learning techniques, Decision Trees and Naïve Bayes, to automatically construct classifiers on the basis of a training set, in which news articles…

Building an asynchronous web-based tool for machine learning classification.

Weber, Griffin; Vinterbo, Staal; Ohno-Machado, Lucila

Proc AMIA Symp · 2002 · English · PubMed Central

Various unsupervised and supervised learning methods including support vector machines, classification trees, linear discriminant analysis and nearest neighbor classifiers have been used to classify high-throughput gene expression data. Simpler and more widely accepted statistical tools have not…

Kernel-based machine learning protocol for predicting DNA-binding proteins

Bhardwaj, Nitin; Langlois, Robert E.; Zhao, Guijun et al.

Nucleic Acids Res · 2005 · English · PubMed Central

…and structural information with moderate accuracy. Here we develop a machine learning protocol for the prediction of DNA-BPs where the classifier is Support Vector Machines (SVMs). Information used for classification is derived from characteristics that include surface and overall composition…

Automating XML Markup using Machine Learning Techniques

Shazia Akhtar; Ronan Reilly; John Dunnion

Journal of Systemics, Cybernetics and Informatics, Vol. 2 No. 5 · 2004 · English · DOAJ

…the Self-Organising Map (SOM) algorithm in conjunction with an inductive learning algorithm, C5.0. The SOM algorithm clusters the XML marked-up documents on a two-dimensional map such that documents having similar content are placed close to each other. The C5.0 algorithm learns and applies markup…

Finding protein sites using machine learning methods

Jaime Leonardo Bobadilla Molina; Fernando Niño; Tobías Mojica

Ingeniería e Investigación, Vol. 23 No. 3 · 2003 · English · DOAJ Scopus

…nonsites that lack this role. 3. A single query site. A support vector machine classifier is built using feature vectors where each component represents a property in a given volume. Validation against an independent test set shows that this recognition approach has high sensitivity and…