the stability of learning rate in neural network identifiers and controllers is one of the challenging issues which attracts great interest from researchers of neural networks. this paper suggests adaptive gradient descent algorithm with stable learning laws for modified dynamic neural network (mdnn) and studies the stability of this algorithm. also, stable learning algorithm for parameters of ...

Incremental learning of classi cation rules from data streams with concept drift is considered. We introduce a new algorithm RILL, which induces rules and single instances, uses bottom-up rule generalization based on nearest rules, and performs intensive pruning of the obtained rule set. Its experimental evaluation shows that it achieves better classi cation accuracy and memory usage than the r...

Nowadays, new methods are required to take advantage of the rich and extensive gold mine of data given the vast content of data particularly created by educational systems. Data mining algorithms have been used in educational systems especially e-learning systems due to the broad usage of these systems. Providing a model to predict final student results in educational course is a reason for usi...

An on-line learning rule, based on the introduction of a matrix momentum term, is presented, aimed at alleviating the computational costs of standard natural gradient learning. The new rule, natural gradient matrix momentum, is analysed in the case of two-layer feed-forward neural network learning viamethods of statistical physics. It appears to provide a practical algorithm that performs as we...

the concept of intelligently controlling the search process of gravitational search algorithm (gsa) is introduced to develop a novel data mining technique. the proposed method is called fuzzy gsa miner (fgsa-miner). at first a fuzzy controller is designed for adaptively controlling the gravitational coefficient and the number of effective objects, as two important parameters which play major ro...

this study proposes a modified version of cultural algorithms (cas) which benefits from rule-based system for influence function. this rule-based system selects and applies the suitable knowledge source according to the distribution of the solutions. this is important to use appropriate influence function to apply to a specific individual, regarding to its role in the search process. this rule ...

Classification rules and rules describing interesting subgroups are important components of descriptive machine learning. Rule learning algorithms typically proceed in two phases: rule refinement selects conditions for specializing the rule, and rule selection selects the final rule among several rule candidates. While most conventional algorithms use the same heuristic for guiding both phases,...

This paper addresses the problem of extracting information from textual documents, either normal documents or web pages. A new approach for extracting complicate information from semi-structured documents is introduced that exploits a successive hierarchical rule-learning algorithm. Through evaluation it is shown that this approach can extract complicate concepts with a much higher precision th...

Unsupervised learning permits the development of algorithms that are able to adapt to a variety of different data sets using the same underlying rules thanks to the autonomous discovery of discriminating features during training. Recently, a new class of Hebbian-like and local unsupervised learning rules for neural networks have been developed that minimise a similarity matching costfunction. T...

We introduce a new large-scale discriminative learning algorithm for machine translation that is capable of learning parameters in models with extremely sparse features. To ensure their reliable estimation and to prevent overfitting, we use a two-phase learning algorithm. First, the contribution of individual sparse features is estimated using large amounts of parallel data. Second, a small dev...