This paper presents a modification of the q-BFGS method for nonlinear unconstrained optimization problems. For this modification, we use simple symmetric positive definite matrix and propose new q-quasi-Newton equation, which is close to ordinary equation in limiting case. uses only first order q-derivatives build an approximate q-Hessian over number iterations. The q-Armijo-Wolfe line search c...

This paper studies the problem of pruning an ensemble of classifiers from a Reinforcement Learning perspective. It contributes a new pruning approach that uses the Q-learning algorithm in order to approximate an optimal policy of choosing whether to include or exclude each classifier from the ensemble. Extensive experimental comparisons of the proposed approach against state-of-the-art pruning ...

The fuzzy c-means clustering algorithm is a useful tool for clustering; but it is convenient only for crisp complete data. In this article, an enhancement of the algorithm is proposed which is suitable for clustering trapezoidal fuzzy data. A linear ranking function is used to define a distance for trapezoidal fuzzy data. Then, as an application, a method based on the proposed algorithm is pres...

We compare scaling properties of several value-function estimation algorithms. In particular, we prove that Q-learning can scale exponentially slowly with the number of states. We identify the reasons of the slow convergence and show that both TD( ) and learning with a xed learning-rate enjoy rather fast convergence, just like the model-based method.

This paper presents the HDG learning algorithm, which uses a hierarchical decomposition of the state space to make learning to achieve goals more efficient with a small penalty in path quality. Special care must be taken when performing hierarchical planning and learning in stochastic domains, because macro-operators cannot be executed ballistically. The HDG algorithm, which is a descendent of ...

In Matveev-Petrov (2017) a q-deformed Robinson-Schensted-Knuth algorithm (qRSK) was introduced. In this article we give reformulations of this algorithm in terms of the Noumi-Yamada description, growth diagrams and local moves. We show that the algorithm is symmetric, namely the output tableaux pairs are swapped in a sense of distribution when the input matrix is transposed. We also formulate a...

The Wiener index of a graph Gis defined as W(G) =1/2[Sum(d(i,j)] over all pair of elements of V(G), where V (G) is the set of vertices of G and d(i, j) is the distance between vertices i and j. In this paper, we give an algorithm by GAP program that can be compute the Wiener index for any graph also we compute the Wiener index of HAC5C7[p, q] and HAC5C6C7[p, q] nanotubes by this program.