• Publications
  • Influence
One-Class SVMs for Document Classification
The SVM approach as represented by Schoelkopf was superior to all the methods except the neural network one, where it was, although occasionally worse, essentially comparable. Expand
One-class document classification via Neural Networks
It is shown how a simple feed-forward neural network can be trained to filter documents under these conditions, and that this method seems to be superior to modified methods, such as Rocchio, Nearest Neighbor, Naive-Bayes, Distance-based Probability and One-Class SVM algorithms. Expand
Decoding the Formation of New Semantics: MVPA Investigation of Rapid Neocortical Plasticity during Associative Encoding through Fast Mapping
It is proposed that fast mapping promotes incidental rapid integration of new associations into existing neocortical semantic networks by activating related, nonoverlapping conceptual knowledge, while hippocampal involvement is less predictive of this kind of learning. Expand
Computational Diagnosis of Parkinson's Disease Directly from Natural Speech Using Machine Learning Techniques
This work shows that early diagnosis of Parkinson's disease is possible solely from the voice signal, and conjecture that such systems can be extended to monitoring and classifying additional neurological diseases and speech pathologies. Expand
Topological constraints and robustness in liquid state machines
It is shown that specifying certain kinds of topological constraints (such as ''small world assumption''), which have been claimed are reasonably plausible biologically, can restore robustness in this sense to LSMs. Expand
Classifying cognitive states of brain activity via one-class neural networks with feature selection by genetic algorithms
It is demonstrated how one-class recognition of cognitive brain functions across multiple subjects can be performed at the 90% level of accuracy via an appropriate choice of features which can be chosen automatically. Expand
Approximating Functions by Neural Networks: A Constructive Solution in the Uniform Norm
The constructive approximation in the uniform norm is more appropriate for a number of examples, and stands in contrast with more standard methods, such as back-propagation, which approximate only in the average error norm. Expand
Classification and biomarker identification using gene network modules and support vector machines
It is demonstrated that more than 90% accuracy can be obtained in classification of selected microarray datasets by integrating the interaction network information with the gene expression information from the microarrays. Expand
Document classification on neural networks using only positive examples (poster session)
In this paper, we show how a simple feed-forward neural network can be trained to filter documents when only positive information is available, and that this method seems to be superior to moreExpand
fMRI Analysis via One-class Machine Learning Techniques
We show how one-class compression Neural Networks and one-class SVM can be applied to fMRI data to learn the classification of brain activity associated with a specific motor activity. For comparisonExpand