Classi cation of cancerous and non-cancerous tissues of serial analysis of gene expression data through various classi ers

Support Vector Machine Classi cation of Microarray Gene Expression Data

Combining Nearest Neighbor Classi ers Through Multiple

Combining multiple classiiers is an eeective technique for improving accuracy. There are many general combining algorithms, such as Bagging or Error Correcting Output Coding, that signiicantly improve classiiers like decision trees, rule learners, or neural networks. Unfortunately, many combining methods do not improve the nearest neighbor classiier. In this paper, we present MFS, a combining a...

Classi cation of brain imaging data

This report presents a technique to enhance existing methods of decoding cognitive states from brain imaging data. The concept is to take the set of voxels in the image and reduce the resolution in each dimension by an optimal factor as determined in the training process. Additionally, the set of voxels are split into smaller subsets of voxels that are localized in time and space. By training c...

Neural Network Classi cation of Non - Uniform Data

| We consider a model of non-uniform data, which resembles typical data for system faults in diagnostic classiication tasks. Phase diagrams illustrate the role reversal of the informators and background as parameters change. With no prior knowledge about the non-uniformity, the Bayesian classiier may perform worse than other neural network classiiers for few examples.

Hierarchical Classi cation of

Classiication is a function that matches a new object with one of the predeened classes. Document classiication is characterized by the large number of attributes involved in the objects (documents). The traditional method of building a single classiier to do all the classiica-tion work would incur a high overhead. Hierarchical classiication is a more eecient method | instead of a single classi...