- Waikato Home
- →
- Library Home
- →
- Research Commons
- →
- Browsing by Author
JavaScript is disabled for your browser. Some features of this site may not work without it.
Browsing by Author "Frank, Eibe"
Now showing items 1-5 of 64
-
(1996)Using rule learning algorithms to model systems has gained considerable interest in the past. The underlying idea of active learning is to learning algorithm influence the selection of training examples. The presented ... -
(Springer, 2008)The much-publicized Netflix competition has put the spotlight on the application domain of collaborative filtering and has sparked interest in machine learning algorithms that can be applied to this sort of problem. The ...
-
(Springer, 2009)Chromatography is an important analytical technique that has widespread use in environmental applications. A typical application is the monitoring of water samples to determine if they contain petroleum. These tests are ... -
(University of Waikato, Department of Computer Science, 2003)Multi-instance learning is commonly tackled using special-purpose algorithms. Development of these algorithms has started because early experiments with standard propositional learners have failed to produce satisfactory ... -
(2011)MITI is a simple and elegant decision tree learner designed for multi-instance classification problems, where examples for learning consist of bags of instances. MITI grows a tree in best-first manner by maintaining a ...
Now showing items 1-5 of 64
Co-authors for Eibe Frank
Supervised by Eibe Frank
Showing up to 5 theses - most recently added to Research Commons first.
-
(University of Waikato, 2012)Most machine learning algorithms require us to set up their parameter values before applying these algorithms to solve problems. Appropriate parameter settings will bring good performance while inappropriate parameter ... -
(University of Waikato, 2011)Error-correcting output codes (ECOCs) have been widely used in many applications for multi-class classification problems. The problem is that ECOCs cannot be ap- plied directly on two-class datasets. The goal of this thesis ... -
(University of Waikato, 2011)Classification learning is a type of supervised machine learning technique that uses a classification model (e.g. decision tree) to predict unknown class labels for previously unseen instances. In many applications it can ... -
(University of Waikato, 2011)Thematic organization of text is a natural practice of humans and a crucial task for today's vast repositories. Clustering automates this by assessing the similarity between texts and organizing them accordingly, grouping ...