Jakub M. Tomczak

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The knowledge extraction is an important element of the e-Health system. In this paper, we introduce a new method for decision rules extraction called Graph-based Rules Inducer to support the medical interview in the diabetes treatment. The emphasis is put on the capability of hidden context change tracking. The context is understood as a set of all factors(More)
In this paper, the problem of detecting the major changes in the stream of service requests is formulated. The change of stream component varies over time and depends on, e.g., a time of a day. The underlying cause of the change is called a context. Hence, at each moment there exists a probability distribution determining the probability of requesting the(More)
In real-life situations characteristics of Web service systems evolve in time. Therefore, change detection techniques become substantial elements of adaptive procedures for Web service systems management, such as resource allocation and anomaly detection methods. In this paper, we propose an on-line change detector which uses the Bayesian inference. We(More)
In the paper a personalization method using Markov model and Bayesian inference is presented. The idea is based on the hypothesis that user's choice of a new decision is influenced by the last made decision. Thus, the user's behaviour could be described by the Markov chain model. The extracted knowledge about users' behaviour is maintained in the transition(More)
In this paper, a fully Bayesian approach to articulated human motion tracking from video sequences is presented. First, a ltering procedure with a low-dimensional manifold is derived. Next, we propose a general framework for approximating this ltering procedure based on the particle lter technique. The low-dimensional manifold can be treated as a(More)
In this paper, we apply Classification Restricted Boltzmann Machine (ClassRBM) to the problem of predicting breast cancer recurrence. According to the Polish National Cancer Registry, in 2010 only, the breast cancer caused almost 25% of all diagnosed cases of cancer in Poland. We propose how to use ClassRBM for predicting breast cancer return and(More)
Application of machine learning to medical diagnosis entails facing two major issues, namely, a necessity of learning comprehensible models and a need of coping with imbalanced data phenomenon. The first one corresponds to a problem of implementing interpretable models, e.g., classification rules or decision trees. The second issue represents a situation in(More)
In this paper, we present the Gaussian process regression as the predictive model for Quality-of-Service (QoS) attributes in Web service systems. The goal is to predict performance of the execution system expressed as QoS attributes given existing execution system, service repository, and inputs, e.g., streams of requests. In order to evaluate the(More)