We describe an algorithm for converting linear support vector machines and any other arbitrary hyperplane-based linear classifiers into a set of non-overlapping rules that, unlike...
This paper develops algorithms to train linear support vector machines (SVMs) when training data are distributed across different nodes and their communication to a centralized no...
Pedro A. Forero, Alfonso Cano, Georgios B. Giannak...
This paper treats tracking as a foreground/background classification problem and proposes an online semisupervised learning framework. Initialized with a small number of labeled ...
The software development process imposes major impacts on the quality of software at every development stage; therefore, a common goal of each software development phase concerns ...
This paper introduces an approach to sentiment analysis which uses support vector machines (SVMs) to bring together diverse sources of potentially pertinent information, including...