Comparison of Support Vector Machine and Back Propagation Neural Network in Evaluating the Enterprise Financial Distress

Recently, applying the novel data mining techniques for evaluating enterprise financial distress has received much research alternation. Support Vector Machine (SVM) and back propagation neural (BPN) network has been applied successfully in many areas with excellent generalization results, such as rule extraction, classification and evaluation. In this paper, a model based on SVM with Gaussian RBF kernel is proposed here for enterprise financial distress evaluation. BPN network is considered one of the simplest and are most general methods used for supervised training of multilayered neural network. The comparative results show that through the difference between the performance measures is marginal; SVM gives higher precision and lower error rates.Comment: 13 pages, 1 figur

Support Vector Machines in High Energy Physics

This lecture will introduce the Support Vector algorithms for classification and regression. They are an application of the so called kernel trick, which allows the extension of a certain class of linear algorithms to the non linear case. The kernel trick will be introduced and in the context of structural risk minimization, large margin algorithms for classification and regression will be presented. Current applications in high energy physics will be discussed.Comment: 11 pages, 12 figures. Part of the proceedings of the Track 'Computational Intelligence for HEP Data Analysis' at iCSC 200

A Study of SVM Kernel Functions for Sensitivity Classification Ensembles with POS Sequences

Freedom of Information (FOI) laws legislate that government documents should be opened to the public. However, many government documents contain sensitive information, such as confidential information, that is exempt from release. Therefore, government documents must be sensitivity reviewed prior to release, to identify and close any sensitive information. With the adoption of born-digital documents, such as email, there is a need for automatic sensitivity classification to assist digital sensitivity review. SVM classifiers and Part-of-Speech sequences have separately been shown to be promising for sensitivity classification. However, sequence classification methodologies, and specifically SVM kernel functions, have not been fully investigated for sensitivity classification. Therefore, in this work, we present an evaluation of five SVM kernel functions for sensitivity classification using POS sequences. Moreover, we show that an ensemble classifier that combines POS sequence classification with text classification can significantly improve sensitivity classification effectiveness (+6.09% F2) compared with a text classification baseline, according to McNemar's test of significance

THE USE OF MOTION SENSORS AND SUPPORT VECTOR MACHINE FOR CLASSIFYING SIMULATED ANKLE SPRAIN AND NORMAL MOTIONS

Ankle sprain is one of the most common sports injuries. Our research team has developed an intelligent system to prevent the injury, and the system relies on a method to identify an ankle sprain motion. The purpose of this study is to increase the accuracy of Support Vector Machine (SVM) in classifying ankle sprain from normal motions and investigate the feasibility to employ SVM in the intelligent system. Fourteen subjects performed trials of (a) walking, (b) vertical jump, (c) stepping down a stair, and (d) jumping off a stair. Data from a motion sensor at the posterior calcaneus were used and trimmed to 230 (0.4s) and 60 (0.12s) window size, and were transformed from time to frequency domain by discrete Fourier Transform. Motion data from eleven subjects (11 out of 14) were used for training the SVM. A Radial Basis Function kernel function was employed in the SVM. Accuracy was tested on the data from another three subjects, which reached 96.1% and 93.1% for window size 230 and 60 respectively

Locally linear approximation for Kernel methods : the Railway Kernel

In this paper we present a new kernel, the Railway Kernel, that works properly for general (nonlinear) classification problems, with the interesting property that acts locally as a linear kernel. In this way, we avoid potential problems due to the use of a general purpose kernel, like the RBF kernel, as the high dimension of the induced feature space. As a consequence, following our methodology the number of support vectors is much lower and, therefore, the generalization capability of the proposed kernel is higher than the obtained using RBF kernels. Experimental work is shown to support the theoretical issues.Support vector machines, Kernel Methods, Classification problems

Bidding Strategy with Forecast Technology Based on Support Vector Machine in Electrcity Market

The participants of the electricity market concern very much the market price evolution. Various technologies have been developed for price forecast. SVM (Support Vector Machine) has shown its good performance in market price forecast. Two approaches for forming the market bidding strategies based on SVM are proposed. One is based on the price forecast accuracy, with which the being rejected risk is defined. The other takes into account the impact of the producer's own bid. The risks associated with the bidding are controlled by the parameters setting. The proposed approaches have been tested on a numerical example.Comment: 8pages, 13figures, paper for the conference "Applications of Physics in Financial Analysis 6th International Conference

Spritz: a server for the prediction of intrinsically disordered regions in protein sequences using kernel machines

Intrinsically disordered proteins have long stretches of their polypeptide chain, which do not adopt a single native structure composed of stable secondary and tertiary structure in the absence of binding partners. The prediction of intrinsically disordered regions in proteins from sequence is increasingly becoming of interest, as the presence of many such regions in the complete genome sequences are discovered and important functional roles are associated with them. We have developed a machine learning approach based on two support vector machines (SVM) to discriminate disordered regions from sequence. The SVM are trained and benchmarked on two sets, representing long and short disordered regions. A preliminary version of Spritz was shown to perform consistently well at the recent biannual CASP-6 experiment [Critical Assessment of Techniques for Protein Structure Prediction (CASP), 2004]. The fully developed Spritz method is freely available as a web server at and

Low Power DSP with wireless monitoring for civil constructions

Noise arising from the construction industry is a major source of noise pollution. Noise from construction sites affects not only the site itself, but also the surrounding area including neighbouring businesses and residents. The duration, complexity, schedule, location, method of construction and type of projects greatly affect the extent of noise impact. Although there are many noise regulation frameworks such as BS 5228, BS 7580 in the UK and 2002/49/EC across Europe to control and mitigate the impact of this construction noise, there is no standardized criteria for assessing construction noise impact. Hence there is a need to identify the extent and magnitude of the noise through using noise monitoring equipment. Such equipment should identify, quantify and differentiate various noise types such as piling, demolition, hammering, reversing truck warning signals and their sources. The system should also provide a noise map of the locality and the surrounding area. This paper describes the development of a custom wireless sensor board for noise identification, monitoring and localisation using a low - power DSP and microcontroller. The system stores noise samples to a local SD memory card for future analysis and wirelessly transmits a summary of significant noise events in real time. This paper describes the result of an initial test of the system on a construction site in Cambridge. The noise event and their efficiency has been compared with high resolution, beamforming technology based SeeSV - S205 audio camera. Future work on the system will include further testing to develop better noise discrimination algorithms.Innovate U