Learn More
The Department of Information Science is one of six departments that make up the Division of Commerce at the University of Otago. The department offers courses of study leading to a major in Information Science within the BCom, BA and BSc degrees. In addition to undergraduate teaching, the department is also strongly involved in postgraduate research(More)
A modularised connectionist model, based on the mixture of experts (ME) algorithm for time series prediction, is introduced. A group of connectionist modules learn to be local experts over some commonly appeared states in a time series. The dynamics for combining the experts is a hidden Markov process, in which the states of a time series are regarded as(More)
In tackling data mining and pattern recognition tasks, finding a compact but effective set of features is often a crucial step in the whole problem solving process. In this paper we present an empirical study on feature selection for classical instrument recognition, using machine learning techniques to select and evaluate features extracted from a number(More)
In this paper we present a case study of co-training to image classification. We consider two scene classification tasks: indoors vs. outdoors and animals vs. sports. The results show that co-training with Naïve Bayes using 8-10 labelled examples obtained only 1.2-1.5% lower classification accuracy than Naïve Bayes trained on the full(More)
The Department of Information Science is one of six departments that make up the School of Business at the University of Otago. The department offers courses of study leading to a major in Information Science within the BCom, BA and BSc degrees. In addition to undergraduate teaching, the department is also strongly involved in postgraduate research(More)
  • Da Deng
  • 2004
Content-based image retrieval techniques have been under intensively research, focusing on extracting effective low level visual features for indexing and enabling fast and accurate retrieval of individual images by matching the feature indexes. In this paper we propose to extend the content-based approach towards the problem of multimedia collection(More)
  • 1