In this paper we present preliminary results stemming from a novel application of Markov Models and Support Vector Machines to splice site classification of Intron-Exon and Exon-I...
Background: Protein-protein interaction (PPI) plays essential roles in cellular functions. The cost, time and other limitations associated with the current experimental methods ha...
Background: The application of machine learning to classification problems that depend only on positive examples is gaining attention in the computational biology community. We an...
Malik Yousef, Segun Jung, Louise C. Showe, Michael...
This paper presents an efficient hybrid feature selection model based on Support Vector Machine (SVM) and Genetic Algorithm (GA) for large healthcare databases. Even though SVM an...
Rick Chow, Wei Zhong, Michael Blackmon, Richard St...
In this paper, we describe the IBM Research system for indexing, analysis, and retrieval of video as applied to the TREC-2007 video retrieval benchmark. This year, focus of the sy...
Murray Campbell, Alexander Haubold, Ming Liu, Apos...
We introduce a method of feature selection for Support Vector Machines. The method is based upon finding those features which minimize bounds on the leave-one-out error. This sear...
Jason Weston, Sayan Mukherjee, Olivier Chapelle, M...
A system has been developed to extract diagnostic information from jet engine carcass vibration data. Support Vector Machines applied to novelty detection provide a measure of how...
The Vicinal Risk Minimization principle establishes a bridge between generative models and methods derived from the Structural Risk Minimization Principle such as Support Vector M...
(Automatic) document classification is generally defined as content-based assignment of one or more predefined categories to documents. Usually, machine learning, statistical patt...
The decision functions constructed by support vector machines (SVM’s) usually depend only on a subset of the training set—the so-called support vectors. We derive asymptotical...