Decision tree model

About: Decision tree model is a research topic. Over the lifetime, 2256 publications have been published within this topic receiving 38142 citations.

Real-time H.264 video encoding in software with fast mode decision and dynamic complexity control

Abstract: This article presents a novel real-time algorithm for reducing and dynamically controlling the computational complexity of an H.264 video encoder implemented in software. A fast mode decision algorithm, based on a Pareto-optimal macroblock classification scheme, is combined with a dynamic complexity control algorithm that adjusts the MB class decisions such that a constant frame rate is achieved. The average coding efficiency of the proposed algorithm was found to be similar to that of conventional encoding operating at half the frame rate. The proposed algorithm was found to provide lower average bitrate and distortion than static complexity scaling.

Computational complexity of decision procedures for polynomials

Abstract: In section I we present theorems which give, among other things, nontrivial lower and upper complexity bounds for decision procedures concerning the existence of bounded natural number solutions to polynomials. The proofs (section III) incorporate essentially all the number theoretic techniques involved in the recent (1970) solution to Hilbert's 10th problem: Does there ex is tan effect i ve pY'ocedure for dec i ding whether a di 0phantine equation (i.e. a multivariable polynomial with integer coefficients) has a solution in the integers?

A serial complexity measure of neural networks

Abstract: The most common methodology of neural network analysis is that of simulation since as of yet there is no common formal framework. Towards this end, one measure of serial algorithms is adopted, i.e., that of serial computational complexity. It is applied to the analysis of neural networks. Various networks are analyzed and their complexity is derived, thus providing insight as to their computational requirements. >

Rough Set Based Decision Tree Model for Classification

Abstract: Decision tree, a commonly used classification model, is constructed recursively following a top down approach (from the general concepts to particular examples) by repeatedly splitting the training data set. ID3 is a greedy algorithm that considers one attribute at a time for splitting at a node. In C4.5, all attributes, barring the nominal attributes used at the parent nodes, are retained for further computation. This leads to extra overheads of memory and computational efforts. Rough Set theory (RS) simplifies the search for dominant attributes in the information systems. In this paper, Rough set based Decision Tree (RDT) model combining the RS tools with classical DT capabilities, is proposed to address the issue of computational overheads. The experiments compare the performance of RDT with RS approach and ID3 algorithm. The performance of RDT over RS approach is observed better in accuracy and rule complexity while RDT and ID3 are comparable.