Learning classifier system is a machine learning technique which combines genetic algorithm with the power of the reinforcement learning paradigm. This rule based system has been inspired by the general principle of Darwinian evolution and cognitive learning. XCS, eXtended Classifier System, is currently considered as state-of-the-art learning classifier systems due to its effectiveness in data...

In this paper principles of extended Kalman filtering theory is developed and applied to simulated on-line electric power systems state estimation in order to trace the operating condition changes through the redundant and noisy measurements. Test results on IEEE 14 - bus test system are included. Three case systems are tried; through the comparing of their results, it is concluded that the pro...

We present our approach to predicting the severity of user posts in a mental health forum. This system was developed to compete in the 2016 Computational Linguistics and Clinical Psychology (CLPsych) Shared Task. Our entry employs a meta-classifier which uses a set of of base classifiers constructed from lexical, syntactic and metadata features. These classifiers were generated for both the tar...

The article deals with the main tasks of energy management systems, as well automation processes systems. problem intellectual verification data entered by users in declarations buildings is considered. To solve problem, a multilayer binary classifier was developed. paper presents results testing effectiveness developed classifier, on training such classifier. set described, An assessment class...

In the several past years, Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) havebecame basic algorithm for state-variables and parameters estimation of discrete nonlinear systems.The UKF has consistently outperformed for estimation. Sometimes least estimation error doesn't yieldwith UKF for the most nonlinear systems. In this paper, we use a new approach for a two variablestate no...

This paper proposes a two-step method for Chinese text categorization (TC). In the first step, a Naïve Bayesian classifier is used to fix the fuzzy area between two categories, and, in the second step, the classifier with more subtle and powerful features is used to deal with documents in the fuzzy area, which are thought of being unreliable in the first step. The preliminary experiment validat...

Agents commonly reason and act over extended periods of time. In some environments , for an agent to solve even a single problem requires many decisions and actions. Consider a robot or animat situated in a real or virtual world, acting to achieve some distant goal; or an agent that controls a sequential process such as a factory production line; or a conversational diagnostic system or rec-omm...

Sensor drift is the most challenging problem in gas sensing at present. We propose a novel two-dimensional classifier ensemble strategy to solve the gas discrimination problem, regardless of the gas concentration, with high accuracy over extended periods of time. This strategy is appropriate for multi-class classifiers that consist of combinations of pairwise classifiers, such as support vector...

This paper presents a radar target recognition method using kernel locally linear embedding (KLLE) and a kernel-based nonlinear representative and discriminative (KNRD) classifier. Locally linear embedding (LLE) is one of the representative manifold learning algorithms for dimensionality reduction. In this paper, LLE is extended by using kernel technique, which gives rises to the KLLE algorithm...

We developed a robust domain selection method and verified its extensibility. An issue in domain selection is its robustness against out-of-grammar utterances. It is essential to generate correct system responses because such utterances often cause domain selection errors. We therefore integrated the topic estimation results and the dialogue history to construct a robust domain classifier. Anot...