Feature logic for web resources customization: Design and implementationSam, Yacine ; Boucelma, Omar ; Colonna, François-Marie
doi: 10.3233/KES-2011-0221pmid: N/A
Users preferences heterogeneity in distributed systems often forces resources suppliers to offer customizable-resources in order to fulfill different customer needs. In this paper we present the formalization and implementation of a Feature Logic based approach that allows customizable-resources description, selection, and composition. In our approach, resources and requests are both specified in a logical framework by feature terms. The feature terms unification technique allows reasoning on these specifications in order to select and possibly compose the resources that are candidate to satisfy a client request.
Feature logic for web resources customization: Design and implementationSam, Yacine; Boucelma, Omar; Colonna, François-Marie
doi: 10.3233/kes-2011-0221pmid: N/A
Users preferences heterogeneity in distributed systems often forces resources suppliers to offer customizable-resources in order to fulfill different customer needs. In this paper we present the formalization and implementation of a Feature Logic based approach that allows customizable-resources description, selection, and composition. In our approach, resources and requests are both specified in a logical framework by feature terms. The feature terms unification technique allows reasoning on these specifications in order to select and possibly compose the resources that are candidate to satisfy a client request.
Finite Newton method for implicit Lagrangian support vector regressionBalasundaram, S.; Kapil,
doi: 10.3233/kes-2011-0222pmid: N/A
In this paper a finite Newton iterative method of solution for solving the implicit Lagrangian Support Vector Regression (SVR) formulation has been proposed. Unlike solving a quadratic programming problem for the case of the standard SVR the solution of the proposed method is obtained by solving a system of linear equations at each iteration of the algorithm. For the linear or nonlinear SVR the finite termination of the proposed method has been established. The algorithm converges from any starting point and does not need any optimization packages. Experiments have been performed on a number of interesting synthetic and real-world datasets. The results obtained by the proposed method are compared with the standard SVR. Similar or better generalization performance of the proposed method clearly demonstrates its effectiveness and applicability.
Finite Newton method for implicit Lagrangian support vector regressionBalasundaram, S. ; Kapil, S.
doi: 10.3233/KES-2011-0222pmid: N/A
In this paper a finite Newton iterative method of solution for solving the implicit Lagrangian Support Vector Regression (SVR) formulation has been proposed. Unlike solving a quadratic programming problem for the case of the standard SVR the solution of the proposed method is obtained by solving a system of linear equations at each iteration of the algorithm. For the linear or nonlinear SVR the finite termination of the proposed method has been established. The algorithm converges from any starting point and does not need any optimization packages. Experiments have been performed on a number of interesting synthetic and real-world datasets. The results obtained by the proposed method are compared with the standard SVR. Similar or better generalization performance of the proposed method clearly demonstrates its effectiveness and applicability.
Towards an immunity-based anomaly detection system for network trafficOkamoto, Takeshi; Ishida, Yoshiteru
doi: 10.3233/kes-2011-0223pmid: N/A
This paper proposes an immunity-based anomaly detection system for network traffic. The system is inspired by the specificity and diversity of the immune system; the system has a user-specific agent for every user, and diverse agents make a decision whether network traffic is normal or abnormal. The system makes use of multiple user profiles, which account for normal user traffic, while conventional anomaly detections have used only the single user profile. The use of multiple profiles leads to an improvement in detection accuracy. In addition, this paper proposes an evaluation framework for the immunity-based anomaly detection system. The evaluation framework is capable of evaluating the differences in detection accuracy between internal and external anomalies. In experiments, the immunity-based method outperformed the conventional method. For internal masquerader detection, the average false acceptance rate was 11.21% with no false alarms. For virus detection, four random-scanning worms and the simulated metaserver worm were detected with no false acceptances and no false alarms, while a simulated passive worm was successfully detected on some of accounts.
Towards an immunity-based anomaly detection system for network trafficOkamoto, Takeshi ; Ishida, Yoshiteru
doi: 10.3233/KES-2011-0223pmid: N/A
This paper proposes an immunity-based anomaly detection system for network traffic. The system is inspired by the specificity and diversity of the immune system; the system has a user-specific agent for every user, and diverse agents make a decision whether network traffic is normal or abnormal. The system makes use of multiple user profiles, which account for normal user traffic, while conventional anomaly detections have used only the single user profile. The use of multiple profiles leads to an improvement in detection accuracy. In addition, this paper proposes an evaluation framework for the immunity-based anomaly detection system. The evaluation framework is capable of evaluating the differences in detection accuracy between internal and external anomalies. In experiments, the immunity-based method outperformed the conventional method. For internal masquerader detection, the average false acceptance rate was 11.21% with no false alarms. For virus detection, four random-scanning worms and the simulated metaserver worm were detected with no false acceptances and no false alarms, while a simulated passive worm was successfully detected on some of accounts.
Optimized code matrix generation for classification of multi-class pattern recognition problems using machine learning techniquesChandrakala, D. ; Sumathi, S. ; Karthi, S.
doi: 10.3233/KES-2011-0224pmid: N/A
The pattern recognition applications like speech recognition, text classification and image recognition result in the solution of multi-class problems. Multi-class problems are reduced into several two class problems using the Machine Learning techniques such as Neural Networks and Support Vector Machines. We propose a hybrid approach for the design of output codes for multi-class pattern recognition problems. This approach has the advantage of taking into account the different aspects that are relevant for a code matrix to achieve good performance. Conventionally, code matrix is designed based on either the features of the problem or the features of the code matrix. The proposed work, focused on designing a new code matrix based on both the features of the problem and code matrix. This model aims at developing a hybrid version of ECOC and adaptive Recursive ECOC with BBO to achieve maximum classification accuracy and minimum computational time. Validation of the results has been performed using non-parametric statistical tests. The statistical results demonstrate that the evolving output codes through BBO provide a general-purpose method for improving the performance of base learners for real world multi-class pattern recognition problems.
Optimized code matrix generation for classification of multi-class pattern recognition problems using machine learning techniquesChandrakala, D.; Sumathi, S.; Karthi, S.
doi: 10.3233/kes-2011-0224pmid: N/A
The pattern recognition applications like speech recognition, text classification and image recognition result in the solution of multi-class problems. Multi-class problems are reduced into several two class problems using the Machine Learning techniques such as Neural Networks and Support Vector Machines. We propose a hybrid approach for the design of output codes for multi-class pattern recognition problems. This approach has the advantage of taking into account the different aspects that are relevant for a code matrix to achieve good performance. Conventionally, code matrix is designed based on either the features of the problem or the features of the code matrix. The proposed work, focused on designing a new code matrix based on both the features of the problem and code matrix. This model aims at developing a hybrid version of ECOC and adaptive Recursive ECOC with BBO to achieve maximum classification accuracy and minimum computational time. Validation of the results has been performed using non-parametric statistical tests. The statistical results demonstrate that the evolving output codes through BBO provide a general-purpose method for improving the performance of base learners for real world multi-class pattern recognition problems.