Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Machine Learning for User Modeling
User Modeling and User-Adapted Interaction
Adaptive Processing of Sequences and Data Structures, International Summer School on Neural Networks, "E.R. Caianiello"-Tutorial Lectures
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Removing biases in unsupervised learning of sequential patterns
Intelligent Data Analysis
Modeling human behavior in user-adaptive systems: Recent advances using soft computing techniques
Expert Systems with Applications: An International Journal
Dimensionality reduction techniques for blog visualization
Expert Systems with Applications: An International Journal
Evolving intelligent algorithms for the modelling of brain and eye signals
Applied Soft Computing
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One of the challenges which must be faced in the field of the information processing is the need to cope with huge amounts of data. There exist many different environments in which large quantities of information are produced. For example, in a command-line interface, a computer user types thousands of commands which can hide information about the behavior of her/his. However, processing this kind of streaming data on-line is a hard problem. This paper addresses the problem of the classification of streaming data from a dimensionality reduction perspective. We propose to learn a lower dimensionality input model which best represents the data and improves the prediction performance versus standard techniques. The proposed method uses maximum dependence criteria as distance measurement and finds the transformation which best represents the command-line user. We also make a comparison between the dimensionality reduction approach and using the full dataset. The results obtained give some deeper understanding in advantages and drawbacks of using both perspectives in this user classifying environment.