Mining sequential rules requires specifying parameters that are often difficult to set (the minimal confidence and minimal support). Depending on the choice of these parameters, current algorithms can become very slow and generate an extremely large amount of results or generate too few results, omitting valuable information. This is a serious problem because in practice users have limited reso...

The architecture of forecasting adaptive wavelet-neuro-fuzzy-network and its learning algorithm for the solving of nonstationary processes forecasting tasks are proposed. The learning algorithm is optimal on rate of convergence and allows to tune both the synaptic weights and dilations and translations parameters of wavelet activation functions. The simulation of developed wavelet-neuro-fuzzy n...

The architecture of adaptive wavelet-neuro-fuzzy-network and its learning algorithm for the solving of nonstationary processes forecasting and emulation tasks are proposed. The learning algorithm is optimal on rate of convergence and allows tuning both the synaptic weights and dilations and translations parameters of wavelet activation functions. The simulation of developed wavelet-neuro-fuzzy ...

We present a class of generative models well suited to modeling perceptual processes and an algorithm for learning their parameters that promises to scale to learning very large models. The models are hierarchical, composed of multiple levels, and allow input only at the lowest level, the base of the hierarchy. Connections within a level are generally local and may or may not be directed. Conne...

Q-learning is a one of the well-known Reinforcement Learning algorithms that has been widely used in various problems. The main contribution of this work is how to speed up the learning in a single agent environment (e.g. the robot). In this work, an attempt to optimize the traditional Q-learning algorithm has been done via using the Repeated Update Q-learning (RUQL) algorithm (the recent state...

Learning of user preferences, as represented by, for example, Conditional Preference Networks (CP-nets), has become a core issue in AI research. Recent studies investigate learning of CP-nets from randomly chosen examples or from membership and equivalence queries. To assess the optimality of learning algorithms as well as to better understand the combinatorial structure of classes of CP-nets, ...

The believability of a virtual world can be increased by improving the behavior of the characters in it. Considering literature, we choose a model developed by Le Hy to generate the behaviors by imitation. The model uses probability distributions to find which decision to choose depending on the sensors. Then actions are chosen depending on the sensors and the decision. The core idea of the mod...

The paper presents a modified structure of Takaga-Sugeno-Kang (TSK) network with a fully automated building and learning algorithm. The modification has resulted in a great reduction of nonlinear parameters of the network (almost three times). The modified network can be initiated using Gustafson-Kessel clustering algorithm. After initiation all parameters are further fine-tuned by an gradient ...

In this paper we propose an algorithm for personalized learning based on a user’s query and a repository of lecture subparts —i.e., learning objects— both are described in a subset of OWL-DL. It works in two steps. First, it retrieves lecture subparts that cover as much as possible the user’s query. The solution is based on the concept covering problem for which we present a modified algorithm....

An algorithm based on the Generalized Hebbian Algorithm is described that allows the singular value decomposition of a dataset to be learned based on single observation pairs presented serially. The algorithm has minimal memory requirements, and is therefore interesting in the natural language domain, where very large datasets are often used, and datasets quickly become intractable. The techniq...