Literature/1995/Yamanishi

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Yamanishi, Kenji (1995). "Randomized Approximate Aggregating Strategies and Their Applications to Prediction and Discrimination," COLT '95: Proceedings of the Eighth Annual Conference on Computational Learning Theory, New York, NY: ACM. pp. 83-90. ISBN:0-89791-723-5 doi>10.1145/225298.225308
  • ACM Digital Library @ [1]

Authors[edit | edit source]

Kenji Yamanishi
NEC Research Institute, Inc., 4 Independence Way, Princeton, NJ


Chronology[edit | edit source]

  • Yamanishi, Kenji (1995). "Randomized Approximate Aggregating Strategies and Their Applications to Prediction and Discrimination," COLT '95: Proceedings of the Eighth Annual Conference on Computational Learning Theory, New York, NY: ACM. pp. 83-90. ISBN:0-89791-723-5 doi>10.1145/225298.225308 [^] [c 1]
  • Literature/1975/Park [^] [c 2]

See also[edit | edit source]

United States Patent
Combustion Prediction and Discrimination Apparatus for an Internal Combustion Engine and Control Apparatus Therefor [2]
Patent Number
5,093,792
Date of Patent
Mar. 3, 1992
Inventors
Masahiro Taki; Matsuei Ueda, both of Aichi, Japan
Assignee
Kabushiki Kaisha Toyota Chuo Kenkyusho, Aishi, Japan
Appl. No.
359,705
Filed
May 31, 1989
Foreign Application Priority Data
May 31, 1988 [JP] Japan ..... 63-133036

Comments[edit | edit source]

Schematic view of document retrieval process, where:
S: System
U: User
d: Document surrogate
D: Document
E: Evidence or query.

The document retrieval system S is to predict the document(s) D most similar to the user query E, and the user U is to discriminate the outcome.

As the one-to-one-scaled map is impractical and implausible, so may be the full text D for System-User communication S-U so that it used to be necessarily surrogated, say, into an abstract or a set of keywords d.

Prediction and discrimination
Excerpt from Michael Ramscar, Research Statement, October 2010 http://psych.stanford.edu/~ramscarlab/Research_Statement.pdf

An aspect of learning theory that has been frequently misunderstood is the role that informativity plays in learning. Although learning models are usually referred to as "associative" -- which might seem to suggest that learning works by simply noting where cues and events co-occur in the environment -- this is not the case. Indeed, for over 40 years the process of "associative learning" has been formally conceived of in terms of prediction and discrimination: learning processes discriminate between predictive cues on the basis of the information they provide. What this means in practice is that learning is driven far more by predicted events that don't occur, than it is by positive pairings of cues and events.

  1. Note "Prediction and Discrimination" in the title. Google this phrase.
  2. See: Fig. 1. Schematic view of retrieval events, and Chapter 3. System vs. User

Notes[edit | edit source]

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Gradient-optical-illusion.svg
The shade of the bar looks invariant in isolation but variant in context, in (favor of) sharp contrast with the color gradient background, hence an innate illusion we have to reasonably interpret and overcome as well as the mirage. Such variance appearing seasonably from context to context may not only be the case with our vision but worldview in general in practice indeed, whether a priori or a posteriori. Perhaps no worldview from nowhere, without any point of view or prejudice at all!

Ogden & Richards (1923) said, "All experience ... is either enjoyed or interpreted ... or both, and very little of it escapes some degree of interpretation."

H. G. Wells (1938) said, "The human individual is born now to live in a society for which his fundamental instincts are altogether inadequate."