We develop a methodology to grasp temporal trend in a stock market that changes year to year, or sometimes within a year depending on numerous factors. For this purpose, we employ ...
In recent years, compressive sensing attracts intensive attentions in the field of statistics, automatic control, data mining and machine learning. It assumes the sparsity of the ...
A style for programming problems from matrix algebra is developed with a familiar example and new tools, yielding high performance with a couple of surprising exceptions. The under...
David S. Wise, Craig Citro, Joshua Hursey, Fang Li...
In this paper, we investigate a simple, mistakedriven learning algorithm for discriminative training of continuous density hidden Markov models (CD-HMMs). Most CD-HMMs for automat...
As the thermal wall becomes the dominant factor limiting VLSI circuit performance, and the interconnect wires become the primary power consumer, power efficiency of onchip data th...
Renshen Wang, Evangeline F. Y. Young, Ronald L. Gr...