We introduce a constructive, incremental learning system for regression problems that models data by means of locally linear experts. In contrast to other approaches, the experts are trained independently and do not compete for data during learning. Only when a prediction for a query is required do the experts cooperate by blending their individual predictions. Each expert is trained by minimiz...

Many data mining applications operate in adversarial environment, for example, webpage ranking in the presence of web spam. A growing number of adversarial data mining techniques are recently developed, providing robust solutions under specific defense-attack models. Existing techniques are tied to distributional assumptions geared towards minimizing the undesirable impact of given attack model...

In this paper we investigate the use of coactive learning in a multitask setting. In coactive learning, an expert presents the learner with a problem and the learner returns a candidate solution. The expert then improves on the solution if necessary and presents the improved solution to the learner. The goal for the learner is to learn to produce solutions which cannot be further improved by th...

We discuss a reinforcement learning framework where learners observe experts interacting with the environment. Our approach is to construct from these observations exploratory policies which favor selection of actions the expert has taken. This imitation strategy can be applied at any stage of learning, and requires neither that information regarding reinforcement be conveyed from the expert to...

BACKGROUND The purpose of this study was to determine errors in measurement of torsional profiles (TP) (torsional femoral angle, torsional tibial angle, and femoral ankle angle) among four orthopedic surgeons, experts, and non-experts in measurement, and the learning curve. METHODS Twenty-six lower extremities of 13 patients with spastic diplegia candidates for femoral/tibial derotational ost...

When humans learn several skills to solve multiple tasks, they exhibit an extraordinary capacity to transfer knowledge between them. We present here the last enhanced version of a bio-inspired reinforcementlearning modular architecture able to perform skill-to-skill knowledge transfer and called ‘TERL Transfer Expert Reinforcement Learning model’. TERL architecture is based on a reinforcement-l...

Bayesian network (BN) learning is difficult task due to the limitation of available data in most engineering fields. On the other hand, BN which constructed bases on experts’ judgments is hard to evaluate and often inaccurate. In this work we introduce a novel method called expert-based structural EM (Ex-SEM) for Bayesian network construction which combines limited objective data and experts’ k...

We present a new online learning algorithm for cumulative discounted gain. This learning algorithm does not use exponential weights on the experts. Instead, it uses a weighting scheme that depends on the regret of the master algorithm relative to the experts. In particular, experts whose discounted cumulative gain is smaller (worse) than that of the master algorithm receive zero weight. We also...