Related Experiment Videos
Second-order SMO improves SVM online and active learning
Tobias Glasmachers1, Christian Igel
1Institut für Neuroinformatik, Ruhr-Universität Bochum, Bochum, Germany. Tobias.Glasmachers@neuroinformatik.ruhr-uni-bochum.de
Abstract:
Iterative learning algorithms that approximate the solution of support vector machines (SVMs) have two potential advantages. First, they allow online and active learning. Second, for large data sets, computing the exact SVM solution may be too time-consuming, and an efficient approximation can be preferable. The powerful LASVM iteratively approaches the exact SVM solution using sequential minimal optimization (SMO). It allows efficient online and active learning. Here, this algorithm is considerably improved in speed and accuracy by replacing the working set selection in the SMO steps. A second-order working set selection strategy, which greedily aims at maximizing the progress in each single step, is incorporated.
Related Concept Videos
Second Order systems I
By reinterpreting the system, one can derive the closed-loop transfer function, which...
Second Order systems II
If ζ...
Observational Learning
Associative Learning
Classical conditioning, also known...
Introduction to Learning
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
Classification of Systems-II