Synopsis
Transductive Reliability Estimation for Individual Classifications in Machine Learning and Data Mining.- Estimating Reliability for Assessing and Correcting Individual Streaming Predictions.- Error Bars for Polynomial Neural Networks.- Robust-Diagnostic Regression: A Prelude for Inducing Reliable Knowledge from Regression.- Reliable Graph Discovery.- Combining Version Spaces and Support Vector Machines for Reliable Classification.- Reliable Ticket Routing in Expert Networks.- Reliable Aggregation on Network Traffic for Web Based Knowledge Discovery.- Sensitivity and Generalization of SVM with Weighted and Reduced Features.- Reliable Gesture Recognition with Transductivie Confidence Machines.- Reliability in A Feature-Selection Process for Intrusion Detection.- The Impact of Sample Size and Data Quality to Classification Reliability.- A Comparative Analysis of Instance-based Penalization Techniques for Classification.- Subsequence Frequency Measurement and its Impact on Reliability of Knowledge Discovery in Single Sequences.- Improving Reliability of Unbalanced Text Mining by Reducing Performance Bias.- Formal Representation and Verification of Ontology Using State Controlled Coloured Petri Nets.- A Reliable System Platform for Group Decision Support under Uncertain Environments.- Index.
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