14748707
Training One-class Support Vector Machines in the Primal Space
In this paper, we proposed a novel Newton-type solver for one-class support vector machines in the primal space directly. Firstly, utilizing reproducing property of kernel and Huber regression function, original constrained quadratic programming is transformed into approximate unconstrained one, which is continuous and twice differentiable. Then, we give a Newton-type training algorithm to solve it. Further analysis shows the computation complexity of our algorithm is identical with theoretical lower bound for solving one-class support vector machines. In the end, experiments on 9 UCI datasets are done to validate the affectivity of proposed algorithm, and when comparing with dual method (LIBSVM),its produces comparative testing accuracy, better training speed, and less support vectors.
36695959
Selective Ensemble Algorithms of Support Vector Machines Based on Constraint Projection
This paper proposes two novel ensemble algorithms for training support vector machines based on constraint projection technique and selective ensemble strategy. Firstly, projective matrices are determined upon randomly selected must-link and cannot-link constraint sets, with which original training samples are transformed into different representation spaces to train a group of base classifiers. Then, two selective ensemble techniques are used to learn the best weighting vector for combining them, namely genetic optimization and minimizing deviation errors respectively. Experiments on UCI datasets show that both proposed algorithms improve the generalization performance of support vector machines significantly, which are much better than classical ensemble algorithms, such as Bagging, Boosting, feature Bagging and LoBag.
38516500
A New Approach of Network Intrusion Detection Using HVDM-Based SOM
The research of applying Self-organizing Maps for intrusion detection is investigated in this paper. A novel approach is presented for enhancing SOM's abilities of identifying temporal network attacks, which combine with FIR filter. Meanwhile, we reconsider the heterogeneous dataset that composed of network connection's features, and select HVDM as the distance function determining the winning neuron during SOM's training and testing. In the end, KDD benchmark dataset is employed to validate the efficiency of our approach, and the results is detection rates of 96.5%, false positive rates of 6.2%, which accounts for good performance of our new approach in intrusion detection fields.
1252619
A fast approximate algorithm for training L1-SVMs in primal space
We propose a novel and fast algorithm to train support vector machines (SVMs) in primal space, which solves an approximate optimization of SVMs with the properties of unconstraint, continuity and twice differentiability by utilizing the Newton optimization technique. Further, we devise a special pre-extracting procedure to speed up the convergence of the algorithm by resorting to a high-quality initial solution. Theoretical studies show that the proposed algorithm produces an @?-approximate solution to standard SVMs and maintains low computational complexity. Experimental results on benchmark data sets demonstrate that our algorithm is much faster than the dual based method such as SVM^l^i^g^h^t while it achieves the similar test accuracy.
22784392
The Application of Non-linear Regression Method to the Functional Connectivity during the Encoding and the Retrieval of Chinese Semantic Memory
Here, the tests about the study of Chinese semantic are presented. The experiments are performed on 9 healthy university students by using the scalp EEG and by using the pictures of Chinese words and idioms as stimuli. The mapping of the power density spectrums are adopted to detect the cognition-related region in the working memory including the memory and the retrieval. The non-linear regression method is available to investigate the interactions in these brain areas and the statistical measurement, ANOVA is utilized to get the significance of interdependencies differences between the working memory and before memory. It is concluded that not only the power density spectrums of gamma oscillations are significantly strengthened, but also the interdependencies in the prefrontal brain region are increased during these memory tasks on all subjects. It is indicated that it is possible to exist the strong feature modulations of attention in the local brain regions during the short-time memory of Chinese semantics.
6828357
Study on Traffic Information Fusion Algorithm Based on Support Vector Machines
Support vector machine (SVM) is a new sort of machine learning method based on structure risk minimization (SRM) principle, which has high generalization capability. Many problems with small samples, nonlinearity or high dimension in pattern recognition could be solved by the method. In this paper, the traffic data on freeway were taken as research objects and an information fusion algorithm based on SVM about freeway incident detection was proposed. A SVM was trained and tested using the data obtained from the simulation under the condition of incident and non-incident. Compared with the multi-layer feed forward neural network (MLF) algorithm trained with the same data, the simulation results showed that the SVM offers a lower misclassification rate, higher correct detection rate and lower false alarm, and it can improve the detection performance
14600536
An Approach to Provisioning E-Commerce Applications with Commercial Components
Component-based development is a trend towards building e-commerce applications. However, commercial components are rarely used during the development. The reason is that existing approaches to selecting and composing components suffer from the problem that the components retrieved usually do not exactly fit with other components in the system being developed. While formal methods can be used to describe and check semantic characteristics to better match components, there are practical limitations which restrict their adoption. We have proposed a framework to support a semantic description and selection of components. We used Simple Component Interface Language (SCIL) to describe user requirements and pre-built components from the current component sources. Specifications in SCIL can be translated to a variety of models including those that have a formal basis. In this paper, we perform a case study of searching commercial components for a generic e-commerce application. We specify the commercial components in SCIL and use two specific tools: jMocha and Alloy Analyser to identify the correct components that suit a particular task
33446676
Voronoi Tessellation Based Rapid Coverage Decision Algorithm for Wireless Sensor Networks
The coverage problem is a fundamental issue in the research of sensor network. A novel Voronoi tessellation based rapid coverage decision algorithm is proposed, in which the region of interest is partitioned into some square grids firstly, and then the complex area coverage problem is transformed into simple vertices coverage problem, on the basis of the good characteristics of Voronoi tessellation such as being able to divide the planner area into a set of simple geometric simplexes. Theoretical analysis and simulation results show that, the new algorithm can decrease the computation complexity of networks, especially for sensor networks with n different sensors, it can solve the coverage problem correctly for any given region of interest with time costs of O(nlogn) only.
45286898
TMAC: Taint-Based Memory Protection via Access Control
Memory corruption attacks account for most parts of malicious attacks toward software security. Recently dynamic taint analysis is proposed and is gaining momentum. This proposed technique attempts to defeat attacks by checking the taintedness and integrity of pointers when accessing memory since vulnerabilities are always motivated by tainting pointers. Unfortunately, there exists some class of attacks without tainting pointers, such as array bounds violation attacks using pointers. In this paper, we propose a novel approach to defeat this kind of undetected attacks using taint-based tracking analysis. Our notion is based on the memory access control, that is, first, we will check the taintedness of the pointers when accessing memory like existing taint-based approaches, second, we will check whether or not the memory area is in the legitimate range of a pointer used to access this memory. Our implementation dose not need source code and is based on Valgrind, hence works on commodity software. To demonstrate our idea, we performed a preliminary empirical experiments, the results are quite promising: TMAC can effectively detect a wide range of attacks, and the average runtime overhead is close to Memcheck, a widely memory error detector.