Support Vector Machines

TL;DR: In this article, three machine learning techniques (artificial neural networks, support vector machines and random forests) were implemented to predict the performance of a wind-powered seawater reverse osmosis (SWRO) desalination plant prototype installed on the island of Gran Canaria (Spain).

TL;DR: This work built a Support Vector Classification model that encompasses the data from two groups of Mexican participants one includes twenty-three diabetic patients and the control group has twenty non-diabetic patients, and the average performance of the classification model was estimated under a rigorous bootstrapping method.

TL;DR: A key learning outcome from the study is the comprehension of the requirement of standard software architecture and Applications Programming Interfaces for continuous research and development of the agent.

TL;DR: In this paper, a two-stage algorithm was proposed to reduce the false alarm rate of voiceprint recognition of electrical faults under noise interference, and the experiment results show that the two stage combination can effectively reduce the true alarm rate in noisy environment and the recall rate only slightly decreased when there is little noise.

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: Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.

TL;DR: There are several arguments which support the observed high accuracy of SVMs, which are reviewed and numerous examples and proofs of most of the key theorems are given.