Support Vector Machine Solvers

TL;DR: It is shown that the χ2 kernel, which has been found to yield the best performance in most applications, also has the most compact feature representation, and is able to obtain a significant performance improvement over current state of the art results based on the intersection kernel.

TL;DR: A comprehensive survey of the characteristics which define and differentiate the types of MIL problems is provided, providing insight on how the problem characteristics affect MIL algorithms, recommendations for future benchmarking and promising avenues for research.

TL;DR: A review on evolution of linear support vector machine classification, its solvers, strategies to improve solvers), experimental results, current challenges and research directions is presented.

TL;DR: A measure of model explanatory power is introduced and it is shown that, although the SVM and CNN models perform similarly in terms of classification accuracy, the latter exhibits a higher level of explainability which makes it more comprehensible for humans and potentially more useful for other applications.

TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?

TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.

TL;DR: A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented, applicable to a wide variety of the classification functions, including Perceptrons, polynomials, and Radial Basis Functions.

TL;DR: Learning with Kernels provides an introduction to SVMs and related kernel methods that provide all of the concepts necessary to enable a reader equipped with some basic mathematical knowledge to enter the world of machine learning using theoretically well-founded yet easy-to-use kernel algorithms.