This article considers the gene ranking algorithm for the microarray data. The rank vector is estimated by classifications of the random data samples. At each iteration, the ranks of genes participating in the successful classification become higher. Unlike other methods of feature selection, the proposed algorithm allows increasing the generality of the classification models by construction of...

In this paper we consider the problem of learning a near-optimal policy in continuous-space, expected total discounted-reward Markovian Decision Problems using approximate policy iteration. We consider batch learning where the training data consists of a single sample path of a fixed, known, persistently-exciting stationary stochastic policy. We derive PAC-style bounds on the difference of the ...

We propose an expectation maximization (EM)based algorithm for semi-blind channel estimation of reciprocal channels in amplify-and-forward (AF) two-way relay networks (TWRNs). By incorporating both data samples and pilots into the estimation, the proposed algorithm provides substantially higher accuracy than the conventional training-based approach. Furthermore, the proposed algorithm has a lin...

Vector Quantization (VQ) plays important role in codebook generation such that the distortion between the original image and the reconstructed image is the minimum. In this paper we present an effective clustering algorithm to generate codebook for vector quantization. In existing algorithm KEVR while splitting the cluster every time new orientation is introduced using error vector sequence. Th...

Gated Recurrent Unit (GRU) is a recently published variant of the Long Short-Term Memory (LSTM) network, designed to solve the vanishing gradient and exploding gradient problems. However, its main objective is to solve the long-term dependency problem in Recurrent Neural Networks (RNNs), which prevents the network to connect an information from previous iteration with the current iteration. Thi...

In this paper, an stable backpropagation algorithm is used to train an online evolving radial basis function neural network. Structure and parameters learning are updated at the same time in our algorithm, we do not make di¤erence in structure learning and parameters learning. It generate groups with an online clustering. The center is updated to achieve the center is near to the incoming data ...

On the basis of least squares support vector machine regression (LSSVR), an adaptive and iterative support vector machine regression algorithm based on chunking incremental learning (CISVR) is presented in this paper. CISVR is an iterative algorithm and the samples are added to the working set in batches. The inverse of the matrix of coefficients from previous iteration is used to calculate the...

F. Shakeri and M. Dehghan in [13] presented the variational iteration method for solving the model describing biological species living together. Here we suggest the differential transform (DT) method for finding the numerical solution of this problem. To this end, we give some preliminary results of the DT and by proving some theorems, we show that the DT method can be easily applied to mentio...

We propose a novel algorithm called graph-shifts for performing image segmentation and labeling. This algorithm makes use of a dynamic hierarchical representation of the image. This representation allows each iteration of the algorithm to make both small and large changes in the segmentation, similar to PDE and split-and-merge methods, respectively. In particular, at each iteration we are able ...