Noise and knowledge acquisition
Published 23 August 1987
Michel Manago, Yves Kodratoff
Citations35
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
This paper presents some methods to detect and treat noise that goes beyond modulating numerical coefficients and shows that noise cannot be viewed as a single entity.
Abstract
In this paper we analyse how noise can affect Knowledge Acquisition from a Machine Learning perspective. We present some methods to detect and treat noise that goes beyond modulating numerical coefficients and show that noise cannot be viewed as a single entity. There are several different types of noise and noise is not only wrong information. I.
Keywords
Computer ScienceNeuroscience
Principles of Artificial Intelligence
3,336 Citations1982Nils J. Nilsson
This classic introduction to artificial intelligence describes fundamental AI ideas that underlie applications such as natural language processing, automatic programming, robotics, machine vision, automatic theorem proving, and intelligent data retrieval.
Cognitive PsychologyUnderstanding natural language
2,802 Citations1972Terry Winograd
A computer system for understanding English that contains a parser, a recognition grammar of English, programs for semantic analysis, and a general problem solving system based on the belief that in modeling language understanding, it must deal in an integrated way with all of the aspects of language—syntax, semantics, and inference.
International Journal of Man-Machine StudiesSimplifying decision trees
2,502 Citations1987J. R. Quinlan
Techniques for simplifying decision trees while retaining their accuracy are discussed, described, illustrated, and compared on a test-bed of decision trees from a variety of domains.
Machine LearningLearning Efficient Classification Procedures and Their Application to Chess End Games
1,106 Citations1983J. R. Quinlan
A series of experiments dealing with the discovery of efficient classification procedures from large numbers of examples is described, with a case study from the chess end game king-rook versus king-knight.
Latin american journal of economicsRule Based Expert Systems: The Mycin Experiments of the Stanford Heuristic Programming Project (The Addison-Wesley series in artificial intelligence)
582 Citations1984Bruce G. Buchanan, Edward H. Shortliffe
Shift of bias for inductive concept learning
288 Citations1984Paul E. Utgoff
It is shown that search for appropriate bias is itself a major part of the learning task, and that mechanical procedures for conducting a well-directed search for an appropriate bias can be created.
Cognitive ScienceA framed painting: The representation of a common sense knowledge fragment
82 Citations1977Eugene Charniak
International Journal of Man-Machine StudiesOntological analysis: an ongoing experiment
43 Citations1987James H. Alexander, Michael J. Freiling +3 more
The experiences of a methodology, called ontological analysis, which provides a technique for performing knowledge- level analysis of a problem space, and the criteria for a formal knowledge-level analysis language are discussed.
Learning object-level and meta-level knowledge in expert systems (machine learning)
6 Citations1985LiMin Fu
An expert program called JAUNDICE has been built, which can diagnose the likely disease and mechanisms of a patient with jaundice and show the developed theory and method of learning are effective in a complex and noisy environment where data may be inconsistent, incomplete, and erroneous.