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Elements of Dimensionality Reduction and Manifold Learning
Dimensionality reduction, also known as manifold learning, is an area of machine learning used for extracting informative features from data for better representation of data or separation between classes. This book presents a cohesive review of linear and nonlinear dimensionality reduction and manifold learning. Three main aspects of dimensionality reduction are covered: spectral dimensionality reduction, probabilistic dimensionality reduction, and neural network-based dimensionality reduction, which have geometric, probabilistic, and information-theoretic points of view to dimensionality reduction, respectively. The necessary background and preliminaries on linear algebra, optimization, an...
Nonlinear Computational Geometry
An original motivation for algebraic geometry was to understand curves and surfaces in three dimensions. Recent theoretical and technological advances in areas such as robotics, computer vision, computer-aided geometric design and molecular biology, together with the increased availability of computational resources, have brought these original questions once more into the forefront of research. One particular challenge is to combine applicable methods from algebraic geometry with proven techniques from piecewise-linear computational geometry (such as Voronoi diagrams and hyperplane arrangements) to develop tools for treating curved objects. These research efforts may be summarized under the...
Large-scale Kernel Machines
Solutions for learning from large scale datasets, including kernel learning algorithms that scale linearly with the volume of the data and experiments carried out on realistically large datasets. Pervasive and networked computers have dramatically reduced the cost of collecting and distributing large datasets. In this context, machine learning algorithms that scale poorly could simply become irrelevant. We need learning algorithms that scale linearly with the volume of the data while maintaining enough statistical efficiency to outperform algorithms that simply process a random subset of the data. This volume offers researchers and engineers practical solutions for learning from large scale ...
Metric Learning
Similarity between objects plays an important role in both human cognitive processes and artificial systems for recognition and categorization. How to appropriately measure such similarities for a given task is crucial to the performance of many machine learning, pattern recognition and data mining methods. This book is devoted to metric learning, a set of techniques to automatically learn similarity and distance functions from data that has attracted a lot of interest in machine learning and related fields in the past ten years. In this book, we provide a thorough review of the metric learning literature that covers algorithms, theory and applications for both numerical and structured data....
PRICAI 2002: Trends in Artificial Intelligence
This book constitutes the refereed proceedings of the 7th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2002, held in Tokyo, Japan in August 2002. The 57 revised full papers presented together with 5 invited contributions and 26 posters were carefully reviewed and selected from 161 submissions. The papers are organized in topical sections on logic and AI foundations, representation and reasoning of actions, constraint satisfaction, foundations of agents, foundations of learning, reinforcement learning, knowledge acquisition and management, data mining and knowledge discovery, neural network learning, learning for robots, multi-agent applications, document analysis, Web intelligence, bioinformatics, intelligent learning environments, face recognition, and multimedia and emotion.
RoboCup 2002: Robot Soccer World Cup VI
RoboCup 2002, the 6th Robot World Cup Soccer and Rescue Competitions and Conference, took place during June 19–25, 2002, at the Fukuoka Dome (main venue) in Fukuoka, Japan. It was, by far, the RoboCup event with the largestnumberofregisteredparticipants(1004persons,distributedin188teams from 29 countries) and visitors (around 120,000 persons). As was done in its previous editions since 1997, the event included several robotic competitions and aninternationalsymposium.Thepapersandposterspresentedatthesymposium constitutethemainpartofthisbook.Leaguereportsinthe?nalsectiondescribe signi?cant advances in each league and the results. The symposium organizers received 76 submissions, among which...
Neural Computation
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