Solving stochastic optimization problems under partial observability, where one needs to adaptively make decisions with uncertain outcomes, is a fundamental but notoriously diffic...
A general framework is proposed for gradient boosting in supervised learning problems where the loss function is defined using a kernel over the output space. It extends boosting ...
In a previous work we have proposed a hybrid Particle Swarm Optimisation/Ant Colony Optimisation (PSO/ACO) algorithm for the discovery of classification rules, in the context of d...
Before the advent of Hidden Markov Models(HMM)-based speech recognition, many speech applications were built using pattern matching algorithms like the Dynamic Time Warping (DTW) ...
Trading rules are widely used by practitioners as an effective means to mechanize aspects of their reasoning about stock price trends. However, due to the simplicity of these rule...