New Support Vector Algorithms
Reads0
Chats0
TLDR
A new class of support vector algorithms for regression and classification that eliminates one of the other free parameters of the algorithm: the accuracy parameter in the regression case, and the regularization constant C in the classification case.Abstract:
We propose a new class of support vector algorithms for regression and classification. In these algorithms, a parameter ν lets one effectively control the number of support vectors. While this can be useful in its own right, the parameterization has the additional benefit of enabling us to eliminate one of the other free parameters of the algorithm: the accuracy parameter epsilon in the regression case, and the regularization constant C in the classification case. We describe the algorithms, give some theoretical results concerning the meaning and the choice of ν, and report experimental results.read more
Citations
More filters
A Personal Email Assistant
TL;DR: The personal email assistant (PEA), which provides a customizable, machinelearning-based environment to support the activities of a major time sink of the authors' daily lives – the processing of email, is reported on.
Journal ArticleDOI
Classification in high-dimensional spectral data: Accuracy vs. interpretability vs. model size
Andreas Backhaus,Udo Seiffert +1 more
TL;DR: This paper evaluates all three of these aspects of interpretability, accuracy, and consistency within the context of several computational intelligence based paradigms for high-dimensional spectral classification of data acquired by hyperspectral imaging and Raman spectroscopy for data mining tasks.
Journal ArticleDOI
Machine Learning Methods Enable Predictive Modeling of Antibody Feature:Function Relationships in RV144 Vaccinees
Ickwon Choi,Amy W. Chung,Todd J. Suscovich,Supachai Rerks-Ngarm,Punnee Pitisuttithum,Sorachai Nitayaphan,Jaranit Kaewkungwal,Robert J. O'Connell,Donald P. Francis,Merlin L. Robb,Nelson L. Michael,Jerome H. Kim,Galit Alter,Margaret E. Ackerman,Chris Bailey-Kellogg +14 more
TL;DR: An extensive investigation of a rich set of data collected from RV144 vaccine recipients is employed to identify and model associations between antibody features (IgG subclass and antigen specificity) and effector function activities and demonstrates via cross-validation that classification and regression approaches can effectively use the antibody features to robustly predict qualitative and quantitative functional outcomes.
Journal ArticleDOI
Applications of machine learning in spectroscopy
TL;DR: This review article focuses on introducing data science subjects to non-specialist spectroscopists, or those unfamiliar with the subject, and explains concepts that are covered in machine learning, such as supervised learning, unsupervised learning, deep learning, and most importantly, the difference between machine learning and artificial intelligence.
Book ChapterDOI
A Review of Kernel Methods in Remote Sensing Data Analysis
TL;DR: This chapter provides a survey of applications and recent theoretical developments of kernel methods in the context of remote sensing data analysis and specific methods developed in the fields of supervised classification, semisupervised classification, target detection, model inversion, and nonlinear feature extraction.
References
More filters
Book
The Nature of Statistical Learning Theory
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?
Journal ArticleDOI
Support-Vector Networks
Corinna Cortes,Vladimir Vapnik +1 more
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.
Book
Matrix Analysis
Roger A. Horn,Charles R. Johnson +1 more
TL;DR: In this article, the authors present results of both classic and recent matrix analyses using canonical forms as a unifying theme, and demonstrate their importance in a variety of applications, such as linear algebra and matrix theory.
Journal ArticleDOI
A Tutorial on Support Vector Machines for Pattern Recognition
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.