Support Vector Machines

Statistical learning algorithms offer a compromise between accuracy and lower computing capacity than the greedy pattern search methods. In the area of supervised machine learning a particularly interesting technique is “Support Vector Machines,” which provides a straightforward classification method trained on a given data set. The method already has many applications in the financial industry mainly linked to credit risk rating, solvency assessment, sentiment analysis, and market time series forecasting.

[1]  Ruud H. Koning,et al.  A Practical Approach to Validating a PD Model , 2009 .

[2]  Bo Pang,et al.  Thumbs up? Sentiment Classification using Machine Learning Techniques , 2002, EMNLP.

[3]  Jason Weston,et al.  Multi-Class Support Vector Machines , 1998 .

[4]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[5]  Christopher J. C. Burges,et al.  A Tutorial on Support Vector Machines for Pattern Recognition , 1998, Data Mining and Knowledge Discovery.

[6]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[7]  Trevor Hastie,et al.  The Elements of Statistical Learning , 2001 .

[8]  Shaio Yan Huang,et al.  Fraud Detection Model by Using Support Vector Machine Techniques , 2013 .

[9]  B. Efron.  Better Bootstrap Confidence Intervals , 1987 .

[10]  Robert Tibshirani,et al.  Bootstrap Methods for Standard Errors, Confidence Intervals, and Other Measures of Statistical Accuracy , 1986 .

[11]  Prabin Kumar Panigrahi,et al.  A Review of Financial Accounting Fraud Detection based on Data Mining Techniques , 2012, ArXiv.

[12]  Rouslan A. Moro,et al.  Support Vector Machines (SVM) as a Technique for Solvency Analysis , 2008 .

[13]  Soushan Wu,et al.  Credit rating analysis with support vector machines and neural networks: a market comparative study , 2004, Decis. Support Syst..

[14]  Gérard Dreyfus,et al.  Single-layer learning revisited: a stepwise procedure for building and training a neural network , 1989, NATO Neurocomputing.