Using a Financial Training Criterion Rather than a Prediction Criterion

Using a Financial Training Criterion Rather than a Prediction Criterion

The application of this work is to decision making with financial time series, using learning algorithms. The traditional approach is to train a model using a prediction criterion, such as minimizing the squared error between predictions and actual values of a dependent variable, or maximizing the likelihood of a conditional model of the dependent variable. We find here with noisy time series t...

Training a Neural Network with a Financial Criterion Rather than a Prediction Criterion

Discriminative Adaptive Training Using the Mpe Criterion

This paper addresses the use of discriminative training criteria for Speaker Adaptive Training (SAT), where both the transform generation and model parameter estimation are estimated using the Minimum Phone Error (MPE) criterion. In a similar fashion to the use of I-smoothing for standard MPE training, a smoothing technique is introduced to avoid over-training when optimizing MPEbased feature-s...

A New Criterion for Screening Medical Applicants

Introduction. In the current screening system, new admissions are based on general intelligence as measured by universities entrance examination. Considering the fact that an important function of medical graduates is to educate the patients how to restore and take care of their health, communicative competence and skills would be, at least, as important as general intelligence. To show that la...

Harris's lopsidependency criterion can be stronger than Shearer's criterion

The Lopsided Lovász Local Lemma (LLLL) is a probabilistic tool which is a cornerstone of the probabilistic method of combinatorics, which shows that it is possible to avoid a collection of “bad” events as long as their probabilities and interdependencies are sufficiently small. The strongest possible criterion that can be stated in these terms is due to Shearer (1985), although it is technicall...