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The Intrusion Detection Systems (IDSs) are one of robust systems which can effectively detect penetrations and attacks. However, they generate large number of alarms most of which are false positives. Fortunately, there are reasons for triggering alarms where most of these reasons are not attacks. In this paper, a new approximation algorithm has developed to group alarms and to produce clusters. Hereafter,...
We present an analytic and geometric view of the sample mean of graphs. The theoretical framework yields efficient subgradient methods for approximating a structural mean and a simple plug-in mechanism to extend existing central clustering algorithms to graphs. Experiments in clustering protein structures show the benefits of the proposed theory.
The paper proposes a theory-based method for estimating the optimal value of k in k-NN classifiers based on a n-sized training set. As expected, experiments show that the suggested k is such that k/n rarr 0 when both k and n tend to infinity, as required by the asymptotical consistency condition. Interestingly, it appears that the generalization error is robust w.r.t. to k when n becomes large (probably...
Non-negative sparse coding (NSC) is a powerful technique for low-rank data approximation, and has found several successful applications in signal processing. However, the temporal dependency, which is a vital clue for many realistic signals, has not been taken into account in its conventional model. In this paper, we propose a general framework, i.e., convolutive non-negative sparse coding (CNSC),...
A new sparse kernel model for spectral clustering is presented. This method is based on the incomplete Cholesky decomposition and can be used to efficiently solve large-scale spectral clustering problems. The formulation arises from a weighted kernel principal component analysis (PCA) interpretation of spectral clustering. The interpretation is within a constrained optimization framework with primal...
In this paper, an efficient feature extraction method named as Constrained Maximum Variance Mapping (CMVM) is developed for dimensionality reduction. The proposed algorithm can be viewed as a linear approximation of multi-manifolds based learning approach, which takes the local geometry and manifold labels into account. After the local scatters have been characterized, the proposed method focuses...
Although supervised learning has been widely used to tackle problems of function approximation and regression estimation, prior knowledge fails to be incorporated into the data-driven approach because the form of input-output data pairs are not applied. To overcome this limitation, focusing on the fusion between rough fuzzy system and very rare samples of input-output pairs with noise, this paper...
Kernel ridge regression (KRR) is a nonlinear extension of the ridge regression. The performance of the KRR depends on its hyperparameters such as a penalty factor C, and RBF kernel parameter sigma. We employ a method called MCV-KRR which optimizes the KRR hyperparameters so that a cross-validation error is minimized. This method becomes equivalent to a predictive approach to Gaussian process. Since...
Gaussian processes is a very promising novel technology that has been applied for both the regression problem and the classification problem. While for the regression problem it yields simple exact solutions, this is not the case for the classification case. The reason is that we encounter intractable integrals. In this paper we propose a new approximate solution for the Gaussian process classification...
The research performed focus on the development of methods of building-up of the intelligent neural network modeling solutions database as well as methods of approximation aiming at empirical knowledge conservation and representation to find the best structure of the artificial neural network (ANN). The learning sample is made up of solutions of approximation of one-dimensional functions defined in...
An application of CAN2 (competitive associative net 2) to plane extraction from 3D range images obtained by a LRF (laser range finder) is presented. The CAN2 basically is a neural net which learns efficient piecewise linear approximation of nonlinear functions, and in this application it is utilized for learning piecewise planner surfaces from the range image. As a result of the learning, the obtained...
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