We conducted the classification subtask at NTCIR6 Patent Retrieval Task using a system based on three document classifiers, namely, a one-vs-rest SVM classifier, multi-topic classifier, and binary Naive Bayes classifier. The multi-topic classifier was constructed on the basis of the maximum margin principle and applied to multiple F-term classification. From the experimental results, this multi...

An intense research around classifier fusion in recent years revealed that combining performance strongly depends on careful selection of classifiers to be combined. Classifier performance depends, in turn, on careful selection of features, which could be further restricted by the subspaces of the data domain. On the other hand, there is already a number of classifier fusion techniques availabl...

This thesis examines the use of positive and negative training data. on a nearestneighbour classifier for hand-drawn geometnc shapes. to improve reliability. A reliiible classifier must feature the ability to reject miss-segrnented and unknown shapes (ie. negative symbols). A recognition system's performance hinges on the performance and reliability of its classifier. In diagram recojnition, wh...

In this paper we present a new Bayesian net­ work model for classification that combines the naive Bayes (NB} classifier and the fi­ nite mixture (FM} classifier. The resulting classifier aims at relaxing the strong assump­ tions on which the two component models are based, in an attempt to improve on their classification performance, both in terms of accuracy and in terms of calibration of the...

Background: There has been much discussion amongst automated software defect prediction researchers regarding use of the precision and false positive rate classifier performance metrics. Aim: To demonstrate and explain why failing to report precision when using data with highly imbalanced class distributions may provide an overly optimistic view of classifier performance. Method: Well documente...

The Fuzzy Hyperline Segment Neural Network (FHLSNN) pattern classifier utilizes fuzzy set as pattern classes in which each fuzzy set is a union of fuzzy set hyperline segments. The Euclidean distance metric is used to compute the distances to decide the degree of membership function. In this paper, the use of other various distance metrics such as Manhattan, Squared Euclidean, Canberra and Cheb...