Inverse maximum flow (IMDF), is among the most important problems in the field ofdynamic network flow, which has been considered the Euclidean norms measure in previousresearches. However, recent studies have mainly focused on the inverse problems under theHamming distance measure due to their practical and important applications. In this paper,we studies a general approach for handling the inv...

This paper presents a two-phase scheme to select reduced number of features from a dataset using Genetic Algorithm (GA) and testing the classification accuracy (CA) of the dataset with the reduced feature set. In the first phase of the proposed work, an unsupervised approach to select a subset of features is applied. GA is used to select stochastically reduced number of features with Sammon Err...

Each t-norm can be identified with its Cayley tomonoid, which consists of pairwise commuting order-preserving functions from the real unit interval to itself. Cayley tomonoids provide an easily manageable, yet versatile tool for the construction of t-norms. To give evidence to this claim, we review and reformulate several construction methods that are known in the literature. We adopt, on the o...

Introduction: Appropriate definition of the distance measure between diffusion tensors has a deep impact on Diffusion Tensor Image (DTI) segmentation results. The geodesic metric is the best distance measure since it yields high-quality segmentation results. However, the important problem with the geodesic metric is a high computational cost of the algorithms based on it. The main goal of this ...

introduction: appropriate definition of the distance measure between diffusion tensors has a deep impact on diffusion tensor image (dti) segmentation results. the geodesic metric is the best distance measure since it yields high-quality segmentation results. however, the important problem with the geodesic metric is a high computational cost of the algorithms based on it. the main goal of this ...

All affinely covariant convex-body-valued valuations on the Sobolev space W (R) are completely classified. It is shown that there is a unique such valuation for Blaschke addition. This valuation turns out to be the operator which associates with each function f ∈W (R) the unit ball of its optimal Sobolev norm. 2000 AMS subject classification: 46B20 (46E35, 52A21,52B45) Let ‖ ·‖ denote a norm on...

A new distance norm [5] is used with the LBG algorithm and its eectiveness in achieving minimum distortion is studied with respect to Euclidean and L 1 norms. Partial Sum and Nearest Neighbouring Distance (NND) approaches are used with this new norm to reduce vector quantization (VQ) codebook design time. The mean square error (MSE) achieved with this new norm is similar to that of the conventi...

We present a polynomial time approximation scheme for Euclidean TSP in <2. Given any n nodes in the plane and > 0, the scheme finds a (1 + )-approximation to the optimum traveling salesman tour in time nO(1= ). When the nodes are in <d, the running time increases to nÕ(logd 2 n)= d 1 . The previous best approximation algorithm for the problem (due to Christofides) achieves a 3=2approximation in...

We consider the problem of separating two sets of points in an Euclidean space with a hyperplane that minimizes the sum of p-norm distances to the plane of points lying on the “wrong” side of the plane. A Variable Neighborhood Search metaheuristic framework is used to determine the plane coefficients. For a set of examples with L1-norm, L2-norm and L∞-norm, for which the exact solution can be c...

Learning vector quantization applying non-standard metrics became quite popular for classification performance improvement compared to standard approaches using the Euclidean distance. Kernel metrics and quadratic forms belong to the most promising approaches. In this paper we consider Minkowski distances (lp-norms). In particular, l1-norms are known to be robust against noise in data, such tha...