Machine Learning Group

Vision

The overall goal of our team is to build state-of-the-art software for developing machine learning (ML) techniques and to apply them to real-world data mining problems.

Objectives

Our objectives are to

• make ML techniques generally available;
• apply them to practical problems that matter to New Zealand industry;
• develop new machine learning algorithms and give them to the world;
• contribute to a theoretical framework for the field.

Machine Learning

An exciting and potentially far-reaching development in computer science is the invention and application of methods of machine learning. These enable a computer program to automatically analyse a large body of data and decide what information is most relevant. This crystallised information can then be used to automatically make predictions or to help people make decisions faster and more accurately.

Software

Our team has incorporated several standard ML techniques into a software "workbench" called WEKA, for Waikato Environment for Knowledge Analysis. With it, a specialist in a particular field is able to use ML to derive useful knowledge from databases that are far too large to be analysed by hand. WEKA's users are ML researchers and industrial scientists, but it is also widely used for teaching. Recently, our team has also worked on MOA, an environment for mining data streams.

Found only on the islands of New Zealand, the Weka is a endangered flightless bird with an inquistive nature. (How should you pronounce WEKA? What does the weka sound like.)

Education

Our team focus on delivering a world-class education, in providing a full and dynamic university experience which is distinctive in character and in pursuing strong international linkages to advance knowledge.

Industry Applications

Our team has developed computerised "machine learning" techniques that can be used to speed up the analysis of data by testing laboratories to help meet these demands. Software exploiting near infra-red(NIR) spectroscopy analysis has reduced the time taken for soil testing from several days to minutes. NIR provides a "blueprint" of a soil sample that can be used to identify the quantity of nitrogen or carbon it contains.