1.8k citations
- Herzberg Institute of AstrophysicsCA20 papers
- University of TorontoCA8 papers
- University of ArizonaUS7 papers
- University of California, BerkeleyUS5 papers
- Lawrence Livermore National LaboratoryUS4 papers
- Université de MontréalCA4 papers
- University of California, Los AngelesUS4 papers
- University of California, Santa CruzUS4 papers
- University of MichiganUS4 papers
- University of WaterlooCA4 papers
- Biotechnology Research InstituteCA3 papers
- California Institute of TechnologyUS3 papers
24 papers · 1 filter
Increasing Evolvability Considered as a Large-Scale Trend in Evolution
Peter D. Turney
Evolvability is the capacity to evolve. This paper introduces a simple computational model of evolvability and demonstrates that, under certain conditions, evolvability can increas…
Robust Classification with Context-Sensitive Features
Peter D. Turney
This paper addresses the problem of classifying observations when features are context-sensitive, especially when the testing set involves a context that is different from the trai…
Data Engineering for the Analysis of Semiconductor Manufacturing Data
Peter D. Turney
We have analyzed manufacturing data from several different semiconductor manufacturing plants, using decision tree induction software called Q-YIELD. The software generates rules f…
Low Size-Complexity Inductive Logic Programming: The East-West Challenge Considered as a Problem in Cost-Sensitive Classification
Peter D. Turney
The Inductive Logic Programming community has considered proof-complexity and model-complexity, but, until recently, size-complexity has received little attention. Recently a chall…
The Identification of Context-Sensitive Features: A Formal Definition of Context for Concept Learning
Peter D. Turney
A large body of research in machine learning is concerned with supervised learning from examples. The examples are typically represented as vectors in a multi-dimensional feature s…
The Management of Context-Sensitive Features: A Review of Strategies
Peter D. Turney
In this paper, we review five heuristic strategies for handling context-sensitive features in supervised machine learning from examples. We discuss two methods for recovering lost…