2D/3D Image Data Analysis for Object Tracking and Classification -- Robot Competence Development by Constructive Learning -- Using Digital Watermarking for Securing Next Generation Media Broadcasts -- A Reduced-Dimension Processor Model -- Hybrid Machine Learning Model for Continuous Microarray Time Series -- An Asymptotic Method to a Financial Optimization Problem -- Analytical Design of Robust Multi-loop PI Controller for Multi-time Delay Processes -- Automatic and Semi-automatic Methods for the Detection of Quasars in Sky Surveys -- Improving Low-Cost Sail Simulator Results by Artificial Neural Networks Models -- Rough Set Approaches to Unsupervised Neural Network Based Pattern Classifier -- A New Robust Combined Method for Auto Exposure and Auto White-Balance -- A Mathematical Analysis Around Capacitive Characteristics of the Current of CSCT: Optimum Utilization of Capacitors of Harmonic Filters -- Harmonic Analysis and Optimum Allocation of Filters in CSCT -- Digital Pen and Paper Technology as a Means of Classroom Administration Relief -- A Conceptual Model for a Network-Based Assessment Security System -- Incorrect Weighting of Absolute Performance in Self-Assessment.

A large international conference on Advances in Machine Learning and Data Analysis was held in UC Berkeley, California, USA, October 22-24, 2008, under the auspices of the World Congress on Engineering and Computer Science (WCECS 2008). This volume contains sixteen revised and extended research articles written by prominent researchers participating in the conference. Topics covered include Expert system, Intelligent decision making, Knowledge-based systems, Knowledge extraction, Data analysis tools, Computational biology, Optimization algorithms, Experiment designs, Complex system identification, Computational modeling, and industrial applications. Advances in Machine Learning and Data Analysis offers the state of the art of tremendous advances in machine learning and data analysis and also serves as an excellent reference text for researchers and graduate students, working on machine learning and data analysis.