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The papers cover various topics and are organized in topical sections on unsupervised learning, and active semisupervised, statistical learning theory, inductive inference, regularized learning, kernel methods, SVM, online and reinforcement learning, learning algorithms and limitations of learning, dimension reduction, the approach another, and open problems.
Computer eBook Details
- ISBN-10: 3540729259
- ISBN-13: 9783540729259
- Publisher: Springer
- Pages: 634
- Date: July 2007
- Series: Lecture Notes in Computer Science