Add to ORKGOptimal Pruning for Unordered Search is a search algorithm that enables complete search through the space of possible disjuncts at the inner level of a covering algorithm. This algorithm takes as inputs an evaluation function, e, a training set, t, and a set of specialisation operators, o. It outputs a set of operators from o that creates a classifier that maximises e with respect to t. While OPUS has exponential worst case time complexity, the algorithm is demonstrated to reach solutions for complex real world domains within reasonable time frames. Indeed, for some domains, the algorithm exhibits greater computational efficiency than common heuristic search algorithms
Several heuristic search algorithms such as A* and breadth-first branch and bound have been develope...
Machine Learning as Massive Search by Richard B. Segal Chairperson of Supervisory Committee: Associ...
When learning classifiers, more extensive search for rules is shown to lead to lower predictive accu...
Optimal Pruning for Unordered Search is a search algorithm that enables complete search through the ...
OPUS is a branch and bound search algorithm that enables efficient admissible search through spaces ...
This paper addresses cost-sensitive classification in the setting where there are costs for measurin...
This paper addresses cost-sensitive classification in the setting where there are costs for measurin...
Determinations are a useful type of functional knowledge representation. Applications include knowle...
Work in machine learning has grown tremendously in the past years, but has had little to no impact o...
Machine Learning (ML) has made significant progress to perform different tasks, such as image classi...
Identifying a small number of features that can represent the data is a known problem that comes up ...
It is well-known that while strict admissibility of heuristics in problem solving guarantees the opt...
Learning Bayesian networks is often cast as an optimization problem, where the computational task is...
We propose three novel pruning techniques to improve the cost and results of inference-aware Differe...
Machine learning is the estimation of the topology (links) of the network, it can be achieved by uti...
Several heuristic search algorithms such as A* and breadth-first branch and bound have been develope...
Machine Learning as Massive Search by Richard B. Segal Chairperson of Supervisory Committee: Associ...
When learning classifiers, more extensive search for rules is shown to lead to lower predictive accu...
Optimal Pruning for Unordered Search is a search algorithm that enables complete search through the ...
OPUS is a branch and bound search algorithm that enables efficient admissible search through spaces ...
This paper addresses cost-sensitive classification in the setting where there are costs for measurin...
This paper addresses cost-sensitive classification in the setting where there are costs for measurin...
Determinations are a useful type of functional knowledge representation. Applications include knowle...
Work in machine learning has grown tremendously in the past years, but has had little to no impact o...
Machine Learning (ML) has made significant progress to perform different tasks, such as image classi...
Identifying a small number of features that can represent the data is a known problem that comes up ...
It is well-known that while strict admissibility of heuristics in problem solving guarantees the opt...
Learning Bayesian networks is often cast as an optimization problem, where the computational task is...
We propose three novel pruning techniques to improve the cost and results of inference-aware Differe...
Machine learning is the estimation of the topology (links) of the network, it can be achieved by uti...
Several heuristic search algorithms such as A* and breadth-first branch and bound have been develope...
Machine Learning as Massive Search by Richard B. Segal Chairperson of Supervisory Committee: Associ...
When learning classifiers, more extensive search for rules is shown to lead to lower predictive accu...
