A distributed search algorithm for solving distributed constraint satisfaction problems (DisCSPs) is presented. The proposed algorithm is composed of multiple search processes (SP...
The distributed constraint satisfaction problem (CSP) is a general formalization used to represent problems in distributed multi-agent systems. To deal with realistic problems, mu...
Using unlabeled data to help supervised learning has become an increasingly attractive methodology and proven to be effective in many applications. This paper applies semi-supervi...
In multimodal function optimization, niching techniques create diversification within the population, thus encouraging heterogeneous convergence. The key to the effective diversif...
Intra- and inter-speaker information, which include acoustical, speaker style, speech rate and temporal variation, despite their critical importance for the verification of claims...
Yongxin Zhang, Adel Iskander Fahmy, Michael S. Sco...
Naive Bayes is one of the most efficient and effective inductive learning algorithms for machine learning and data mining. Its competitive performance in classification is surpris...
We illustrate that Web searches can often be utilized to generate background text for use with text classification. This is the case because there are frequently many pages on the...
This paper presents work that uses Transductive Latent Semantic Indexing (LSI) for text classification. In addition to relying on labeled training data, we improve classification ...
Bayesian network is a popular modeling tool for uncertain domains that provides a compact representation of a joint probability distribution among a set of variables. Even though ...