SERUBA – A new search and learning technology for the internet and intranets.

The paper describes a multi-lingual, ontology-based system for user support and learning in very large, non-domain specific network environments. The languages implemented are English, Spanish, French and Gennan. SERUBA, named after its SEmantic and RUle-BAsed approach, will hit the Internet market early in 2001

A Commodity Trading Model Based on a Neural Network-Expert System Hybrid

Demonstrates a system that combines a neural network approach with an expert system to provide superior performance compared to either approach alone. Learning capability is provided in a software-based approach to commodity trading systems. The authors used the backpropagation network with some parameters selected experimentally. They used a human expert to implicitly define patterns, using hindsight, that an intelligent system might have been able to use for an accurate prediction. Desired outputs were found by a combination of observing the behavior of technical indices that normally precede a certain kind of market behavior, and by observing the actual market behavior in retrospect. Thus, the network learns to give signals based on data that look favorable to a human expert. The authors show the results of a rule-based daily trading system that has been augmented by a neural network market predictor

A Hybrid Machine Learning System for Stock Market Forecasting

A hybrid machine learning system based on Genetic Algorithm (GA) and Time Series Analysis is proposed. In stock market, a technical trading rule is a popular tool for analysts and users to do their research and decide to buy or sell their shares. The key issue for the success of a trading rule is the selection of values for all parameters and their combinations. However, the range of parameters can vary in a large domain, so it is difficult for users to find the best parameter combination. In this paper, we present the Genetic Algorithm (GA) to overcome the problem in two steps. First, setting a sub-domain of the parameters with GA. Second, finding a near optimal value in the sub domain with GA and Time Series Analysis in a very reasonable time

An Intelligent Model for Stock Investment with Buffett Strategy, Classifier System, Neural Network and Linear Programming

“The Intelligent Model for Stock Investment with Buffett Strategy, Classifier System, Neural Network and Linear Programming” was studied for developing an intelligent model which can learn more knowledge regarding to stock investment with artificial intelligence technology. Classifier system, neural network, fundamental financial investment factors and linear programming are the fundamental components for the research. Knowledge transformation and genetic evolution capability was discussed in the article, too. Furthermore, the investment strategy developed by Warren E. Buffett[17], the great financial investment master, was the major knowledge which was practiced in the article. For realizing more detail about learning system, a lot of topics regarding to artificial intelligence were discussed in advanced, including “A Market-Based Rule Learning System” [1], “Dynamic Trading Strategy Learning Model using Learning Classifier System” [2], “Nonlinear Index Prediction” [3], “Financial Decision Support with Hybrid Genetic and Neural Based Modeling Tool” [4] and “Fuzzy Interval methods in Investment risk Appraisal” [5]. According to the study mentioned above, the ideas to give intelligent model, especially with genetic algorithm, bring the direction for the advanced financial investment strategy and operation. Therefore, it was why a novel intelligent model with Buffett strategy, classifier system, neural network and linear programming proposed in the article

ABA/CEELI’s clinical legal education programme in Serbia

The goal of the CEELI Legal Education Reform Program in Serbia has been to assist Serbian law faculties in reforming the curriculum so that law students become lawyers who can contribute to the development of the rule of law and the transition to a market economy. As a country in transition, Serbia must prepare future lawyers who are capable of absorbing and implementing the breadth of changes underway in the legal system. Unfortunately, in both its pedagogical methodology and its resources, the education predominantly provided to law students in Serbia is woefully inadequate. Education is typically based on memorisation of code provisions, with little opportunity for practice-based learning or creative thinking, and many of the textbooks used by law students date back to the socialist era