Machine Learning
Nearest Neighbors by Neighborhood Counting
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Study of the Neighborhood Counting Similarity
IEEE Transactions on Knowledge and Data Engineering
On the similarity metric and the distance metric
Theoretical Computer Science
A Lattice machine approach to automated casebase design: marrying lazy and eager learning
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A flexible and robust similarity measure based on contextual probability
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
A Multidimensional Sequence Approach to Measuring Tree Similarity
IEEE Transactions on Knowledge and Data Engineering
A novel clustering method based on spatial operations
BNCOD'06 Proceedings of the 23rd British National Conference on Databases, conference on Flexible and Efficient Information Handling
Pattern Recognition Letters
| Hi-index | 0.00 |
In this paper we review Lattice Machine, a learning paradigm that “learns” by generalising data in a consistent, conservative and parsimonious way, and has the advantage of being able to provide additional reliability information for any classification. More specifically, we review the related concepts such as hyper tuple and hyper relation, the three generalising criteria equilabelledness, maximality, and supportedness as well as the modelling and classifying algorithms. In an attempt to find a better method for classification in Lattice Machine, we consider the contextual probability which was originally proposed as a measure for approximate reasoning when there is insufficient data. It was later found to be a probability function that has the same classification ability as the data generating probability called primary probability. It was also found to be an alternative way of estimating the primary probability without much model assumption. Consequently, a contextual probability based Bayes classifier can be designed. In this paper we present a new classifier that utilises the Lattice Machine model and generalises the contextual probability based Bayes classifier. We interpret the model as a dense set of data points in the data space and then apply the contextual probability based Bayes classifier. A theorem is presented that allows efficient estimation of the contextual probability based on this interpretation. The proposed classifier is illustrated by examples.