Directional Support Vector Machines

Several phenomena are represented by directional—angular or periodic—data; from time references on the calendar to geographical coordinates. These values are usually represented as real values restricted to a given range (e.g., [ 0 , 2 π ) ), hiding the real nature of this information. In order to handle these variables properly in supervised classification tasks, alternatives to the naive Bayes classifier and logistic regression were proposed in the past. In this work, we propose directional-aware support vector machines. We address several realizations of the proposed models, studying their kernelized counterparts and their expressiveness. Finally, we validate the performance of the proposed Support Vector Machines (SVMs) against the directional naive Bayes and directional logistic regression with real data, obtaining competitive results.

[1]  Jaime S. Cardoso,et al.  Discriminative directional classifiers , 2016, Neurocomputing.

[2]  Paulo Cortez,et al.  A Proactive Intelligent Decision Support System for Predicting the Popularity of Online News , 2015, EPIA.

[3]  Jaime S. Cardoso,et al.  Social Signaling Descriptor for Group Behaviour Analysis , 2015, IbPRIA.

[4]  Concha Bielza,et al.  Directional naive Bayes classifiers , 2015, Pattern Analysis and Applications.

[5]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[6]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[7]  Shyam Visweswaran,et al.  Deep Multiple Kernel Learning , 2013, 2013 12th International Conference on Machine Learning and Applications.

[8]  Kanti V. Mardia,et al.  Mixtures of concentrated multivariate sine distributions with applications to bioinformatics , 2012 .

[9]  Suvrit Sra,et al.  A short note on parameter approximation for von Mises-Fisher distributions: and a fast implementation of Is(x) , 2012, Comput. Stat..

[10]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[11]  Guilherme A. Barreto,et al.  Short-term memory mechanisms in neural network learning of robot navigation tasks: A case study , 2009, 2009 6th Latin American Robotics Symposium (LARS 2009).

[12]  Cordelia Schmid,et al.  Applying Color Names to Image Description , 2007, 2007 IEEE International Conference on Image Processing.

[13]  Yoram Singer,et al.  Pegasos: primal estimated sub-gradient solver for SVM , 2007, ICML '07.

[14]  Chih-Jen Lin,et al.  Working Set Selection Using Second Order Information for Training Support Vector Machines , 2005, J. Mach. Learn. Res..

[15]  Inderjit S. Dhillon,et al.  Clustering on the Unit Hypersphere using von Mises-Fisher Distributions , 2005, J. Mach. Learn. Res..

[16]  Petr Savický,et al.  Methods for multidimensional event classification: A case study using images from a Cherenkov gamma-ray telescope , 2004 .

[17]  Ian T. Jolliffe,et al.  Fitting mixtures of von Mises distributions: a case study involving sudden infant death syndrome , 2003, Comput. Stat. Data Anal..

[18]  H. A. Guvenir,et al.  A supervised machine learning algorithm for arrhythmia analysis , 1997, Computers in Cardiology 1997.

[19]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[20]  Amnon Shashua,et al.  Ranking with Large Margin Principle: Two Approaches , 2002, NIPS.

[21]  W. L. Kovach,et al.  Quantitative methods for the study of lycopod megaspore ultrastructure , 1989 .

[22]  Vladimir Vapnik,et al.  Chervonenkis: On the uniform convergence of relative frequencies of events to their probabilities , 1971 .