Data Mining Discretization Methods and Performances (PDF
... Discretization process is known to be one of the most important data preprocessing tasks in data mining. Presently, many discretization methods are available. These include Boolean Reasoning, Equal Frequency Binning, Entropy, and others. Each method is developed for specific problems or domain area. ...
... Discretization process is known to be one of the most important data preprocessing tasks in data mining. Presently, many discretization methods are available. These include Boolean Reasoning, Equal Frequency Binning, Entropy, and others. Each method is developed for specific problems or domain area. ...
RWA Problem formulations
... We are required to determine the different light paths to be established, the routes over which they are set up and also determine the wavelengths that should be assigned to these lightpaths so that the maximum number of lightspaths may be established. Or to achieve a minimum number of wavelengths i ...
... We are required to determine the different light paths to be established, the routes over which they are set up and also determine the wavelengths that should be assigned to these lightpaths so that the maximum number of lightspaths may be established. Or to achieve a minimum number of wavelengths i ...
A New Discontinuous Petrov-Galerkin Method with Optimal Test
... FD methods approximated the conservation law, using some numerical flux to reconstruct approximations to the derivative at a point. Finite volume (FV) methods are similar to finite difference methods, but approximate the integral version of a conservation law as opposed to the differential form. FD ...
... FD methods approximated the conservation law, using some numerical flux to reconstruct approximations to the derivative at a point. Finite volume (FV) methods are similar to finite difference methods, but approximate the integral version of a conservation law as opposed to the differential form. FD ...
On the Implementation of MIPS
... for handling objects, action, predicates, and facts of the planning domain. data.symbFact: Maintains symbolic facts as found in precondition and effect lists of the operators. step.parse: Reads input files and builds the corresponding data structures. For this purpose a simple parser for processing ...
... for handling objects, action, predicates, and facts of the planning domain. data.symbFact: Maintains symbolic facts as found in precondition and effect lists of the operators. step.parse: Reads input files and builds the corresponding data structures. For this purpose a simple parser for processing ...
Problem 1. If we increase the length of each edge of a cube by 100
... We will show that 19 is the smallest possible value of n. First note that only one digit can appear in a palindromic chain odd number of times (namely the middle one). Clearly, for n ≤ 9 this condition cannot be satisfied. Similarly, for 10 ≤ n ≤ 18 both digits 0 and 9 appear exactly once, thus such ...
... We will show that 19 is the smallest possible value of n. First note that only one digit can appear in a palindromic chain odd number of times (namely the middle one). Clearly, for n ≤ 9 this condition cannot be satisfied. Similarly, for 10 ≤ n ≤ 18 both digits 0 and 9 appear exactly once, thus such ...
Hybrid Inductive Machine Learning: An Overview of CLIP Algorithms
... The AQ15 algorithm performs a top-down search through all positive examples and generates a decision rule for each class in turn. At each step it starts with selecting one positive example (the seed) and generates all complexes (a star) that covers the seed, but does not cover any negative example. ...
... The AQ15 algorithm performs a top-down search through all positive examples and generates a decision rule for each class in turn. At each step it starts with selecting one positive example (the seed) and generates all complexes (a star) that covers the seed, but does not cover any negative example. ...
A polynomial time algorithm for Rayleigh ratio on
... processor, so as to minimize the amount of required communication between the processors, while increasing the inter-grid similarity within each subgrid. In order to partition the grid points into more than two processors, one iteratively repeats the partition generated by the optimal solution to Ch ...
... processor, so as to minimize the amount of required communication between the processors, while increasing the inter-grid similarity within each subgrid. In order to partition the grid points into more than two processors, one iteratively repeats the partition generated by the optimal solution to Ch ...
PDF 2 Heat Equation
... We will give specific examples below where we consider some of these boundary conditions. First, however, we present the technique of separation of variables. This technique involves looking for a solution of a particular form. In particular, we look for a solution of the form u(x, t) = X(x)T (t) fo ...
... We will give specific examples below where we consider some of these boundary conditions. First, however, we present the technique of separation of variables. This technique involves looking for a solution of a particular form. In particular, we look for a solution of the form u(x, t) = X(x)T (t) fo ...
Solving 3D incompressible Navier-Stokes equations on hybrid CPU
... of problem. More specifically for GPU architectures, several NS solvers have been developed in recent years (see e.g. [4] that uses finite element method). In this paper, we consider a finite difference discretization of the prediction-projection method [5], and we present a hybrid solver for the re ...
... of problem. More specifically for GPU architectures, several NS solvers have been developed in recent years (see e.g. [4] that uses finite element method). In this paper, we consider a finite difference discretization of the prediction-projection method [5], and we present a hybrid solver for the re ...
Genetic algorithm
In the field of artificial intelligence, a genetic algorithm (GA) is a search heuristic that mimics the process of natural selection. This heuristic (also sometimes called a metaheuristic) is routinely used to generate useful solutions to optimization and search problems. Genetic algorithms belong to the larger class of evolutionary algorithms (EA), which generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover.