Boosting auxiliary task guidance: a probabilistic approach

Stock Market Fraud Detection, A Probabilistic Approach

In order to have a fair market condition, it is crucial that regulators continuously monitor the stock market for possible fraud and market manipulation. There are many types of fraudulent activities defined in this context. In our paper we will be focusing on "front running". According to Association of Certified Fraud Examiners, front running is a form of insider information and thus is very ...

Boosting Probabilistic Choice Operators

Probabilistic Choice Operators (PCOs) are convenient tools to model uncertainty in CP. They are useful to implement randomized algorithms and stochastic processes in the concurrent constraint framework. Their implementation is based on the random selection of a value inside a finite domain according to a given probability distribution. Unfortunately, the probabilistic choice of a PCO is usually...

Multi-Task Boosting by Exploiting Task Relationships

Multi-task learning aims at improving the performance of one learning task with the help of other related tasks. It is particularly useful when each task has very limited labeled data. A central issue in multi-task learning is to learn and exploit the relationships between tasks. In this paper, we generalize boosting to the multi-task learning setting and propose a method called multi-task boos...

A Probabilistic Approach to the Medical Retrieval Task

Boosting in Probabilistic Neural Networks

The basic idea of boosting is to increase the pattern recognition accuracy by combining classifiers which have been derived from differently weighted versions of the original training data. It has been verified in practical experiments that the resulting classification performance can be improved by increasing the weights of misclassified training samples. However, in statistical pattern recogn...