The TREC-8 ltering track measures the ability of systems to build persistent user pro les which successfully separate relevant and non-relevant documents. It consists of three major subtasks: adaptive ltering, batch ltering, and routing. In adaptive ltering, the system begins with only a topic statement and must learn a better pro le from on-line feedback. Batch ltering and routing are more tra...

Causal structure learning algorithms have focused on learning in ”batch-mode”: i.e., when a full dataset is presented. In many domains, however, it is important to learn in an online fashion from sequential or ordered data, whether because of memory storage constraints or because of potential changes in the underlying causal structure over the course of learning. In this paper, we present TDSL,...

Active learning involves selecting unlabeled data items to label in order to best improve an existing classifier. In most applications, batch mode active learning, where a set of items is picked all at once to be labeled and then used to re-train the classifier, is most feasible because it does not require the model to be re-trained after each individual selection and makes most efficient use o...

Along with the explosive increase of data and information, incremental learning ability has become more and more important for machine learning approaches. The online algorithms try to forget irrelevant information instead of synthesizing all available information (as opposed to classic batch learning algorithms). Nowadays, combining classifiers is proposed as a new direction for the improvemen...

High Performance Computing (HPC) applications are essential for scientists and engineers to create and understand models and their properties. These professionals depend on the execution of large sets of computational jobs that explore combinations of parameter values. Avoiding the execution of unnecessary jobs brings not only speed to these experiments, but also reductions in infrastructure us...

Structure learning algorithms for graphical models have focused almost exclusively on stable environments in which the underlying generative process does not change; that is, they assume that the generating model is globally stationary. In real-world environments, however, such changes often occur without warning or signal. Real-world data often come from generating models that are only locally...

This paper highlights the crucial role that modern machine learning techniques can play in the optimization of treatment strategies for patients with chronic disorders. In particular, we focus on the task of optimizing a deep-brain stimulation strategy for the treatment of epilepsy. The challenge is to choose which stimulation action to apply, as a function of the observed EEG signal, so as to ...

Scheduling decisions in the diffusion and cleaning area can be crucial for the overall performance of a semiconductor manufacturing facility. This area includes complex production constraints and long processing durations. Consequently, we want to optimize scheduling decisions in this area while taking all relevant real-world constraints into account. An important property of machines in this w...

This paper considers the minimization of makespan in unrelated parallel batch processing machines scheduling problem with considering non-identical job size and dynamic ready time. The considered have different capacity speed. Each machine processes a number jobs as at same time so that machine’s is not exceeded. are equal to largest batch, respectively. In this paper, Mixed Integer Linear Prog...