http://hdl.handle.net/10453/11566 Wed, 19 Jun 2013 07:44:19 GMT 2013-06-19T07:44:19Z http://hdl.handle.net/10453/12099 Classification Techniques in Pattern Recognition Zheng Lihong; He Xiangjian Vaclav Skala In this paper, we review some pattern recognition schemes published in recent years. After giving the general processing steps of pattern recognition, we discuss several methods used for steps of pattern recognition such as Principal Component Analysis (PCA) in feature extraction, Support Vector Machines (SVM) in classification, and so forth. Different kinds of merits are presented and their applications on pattern precognition are given. The objective of this paper is to summarize and compare some of the methods for pattern recognition, and future research issues which need to be resolved and investigated further are given along with the new trends and ideas. Sat, 01 Jan 2005 00:00:00 GMT http://hdl.handle.net/10453/12099 2005-01-01T00:00:00Z http://hdl.handle.net/10453/12098 Evaluation of the Northern Territory Library's Libraries and Knowledge Centres Model Nakata Nicholas; Nakata Victoria; Anderson Jane; Hart Victor; Hunter Jane; Smallacombe Sonia; Richmond Cate; Lloyd Brian; Maynard Gibby Evaluation of the Northern Territory Library's model for Libraries and Knowledge Centres in Indigenous communities. Sun, 01 Jan 2006 00:00:00 GMT http://hdl.handle.net/10453/12098 2006-01-01T00:00:00Z http://hdl.handle.net/10453/2763 A human-friendly MAS for mining stock data Ni Jiarui; Zhang Chengqi Butz, C; Nguyen, T; Takama, Y; Cheung, W; Cheung, Y Mining stock data can be beneficial to the participants and researchers in the stock market. However, it is very difficult for a normal trader or researcher to apply data mining techniques to the data on his own due to the complexity involved in the whole data mining process. In this paper, we present a multi-agent system that can help users easily deal with their data mining jobs on stock data. This system guides users to specify their mining tasks by simply specifying the data sets to be mined and selecting pre-defined and/or user-added data mining agents. This approach offers normal traders a practical and flexible solution to mining stock data. Sun, 01 Jan 2006 00:00:00 GMT http://hdl.handle.net/10453/2763 2006-01-01T00:00:00Z http://hdl.handle.net/10453/2756 Efficient frequent itemsets mining by sampling Zhang Chengqi; Zhao Yanchang; Zhang Shichao Li, Y; Looi, M; Zhong, N As the first stage for discovering association rules, frequent itemsets mining is an important challenging task for large databases. Sampling provides an efficient way to get approximating answers in much shorter time. Based on the characteristics of frequent itemsets counting, a new bound for sampling is proposed, with which less samples are necessary to achieve the required accuracy and the efficiency is much improved over traditional Chernoff bounds. Sun, 01 Jan 2006 00:00:00 GMT http://hdl.handle.net/10453/2756 2006-01-01T00:00:00Z