Within the statistical learning framework, this paper studies the regression model associated with the correntropy induced losses. The correntropy, as a similarity measure, has been frequently employed in signal processing and pattern recognition. Motivated by its empirical successes, this paper aims at presenting some theoretical understanding towards the maximum correntropy criterion in regre...

The maximum correntropy criterion (MCC) has recently been successfully applied to adaptive filtering. Adaptive algorithms under MCC show strong robustness against large outliers. In this work, we apply the MCC criterion to develop a robust Hammerstein adaptive filter. Compared with the traditional Hammerstein adaptive filters, which are usually derived based on the well-known mean square error ...

Let finite source and reproduction alphabets X and Y and a distortion measure d : X ×Y → [0,∞) be given. We study the minimum asymptotic rate required to describe a source distributed over X within a (given) distortion threshold D at every sample. The problem is hence a min-max problem, and the distortion measure is extended to vectors as follows: for x ∈ X, y ∈ Y, d(x, y) = maxi d(xi, yi). In ...

Effective seizure detection from long-term EEG is highly important for seizure diagnosis. Existing methods usually design the feature and classifier individually, while little work has been done for the simultaneous optimization of the two parts. This work proposes a deep network to jointly learn a feature and a classifier so that they could help each other to make the whole system optimal. To ...

In this paper, we study the problem of feature extraction for pattern classification applications. RELIEF is considered as one of the best-performed algorithms for assessing the quality of features for pattern classification. Its extension, local feature extraction (LFE), was proposed recently and was shown to outperform RELIEF. In this paper, we extend LFE to the nonlinear case, and develop a ...

Article history: Received 28 July 2008 Received in revised form 2 March 2009 Accepted 9 March 2009

We study a class of scheduling problems involving the maximum lateness criterion and an element of batching For all the problems that we examine algorithms appear in the literature which consist of a sorting step to determine an optimal job sequence followed by a dynamic programming step which determines the optimal batches In each case the dynamic program is based on a backward recursion of wh...

We consider the motion estimation problem in video coding. In our previous work [10], we proposed a new motion estimation method where motion estimation is formulated as an optimization problem and an adaptive system under the minimum error entropy criterion is used for motion estimation. In this paper, we develop an adaptive system under the criterion of maximum mutual information to address t...

Four heuristic algorithms are developed for solving the generalized version of tardiness flow shop problems. We consider the generalized tardiness flow shop model with minimization of the total tardiness as its performance measure. We modify the concept of cost over time (COVERT) for the generalized version of the flow shop tardiness model and employ this concept for developing four algorithms....

We consider the problem of scheduling of n jobs to minimize the total earliness and tardiness penalty. The scheduling objective is to minimize the sum of job dependent earliness and tardiness penalties.. After determining the scheduling for single machine common due date, using neural network, the result shows that the neural network solution performs very well.