Raul E. Valdes-Perez. Some recenthuman/computer discoveries in science and what accounts for them. AI Magazine, 16(3):37--44, 1995. Home/Search Document Not in Database Summary Related Articles Check |
This paper is cited in the following contexts:
Integrating Many Techniques for Discovering Structure in Data - Gregory, Cohen (Correct)
.... [2] Second, integrated systems have the advantage of supplementing one kind of result with others, information which often constitutes an explanation of previous findings (e.g. 7] Finally,interesting structure is often discovered in the process of shifting from one representation to another [10]. Thus, there is a significantadvantage to integrated discovery systems. This paper describes our approachtotheintegration of analysis techniques and a system, called the Scientist s Empirical Assistant (SEA) that implements this view. Section 2 presents an example of complex data analysis that ....
Raul E. Valdes-Perez. Some recenthuman/computer discoveries in science and what accounts for them. AI Magazine, 16(3):37--44, 1995.
Exploiting Background Knowledge in Automated Discovery - Aronis, Provost, al. (1996) (4 citations) (Correct)
....linking examples of cases of plant exposures to databases of botanical, geographical, and climate background knowledge. Introduction Discoveries made by computer programs have been characterized as human computer discoveries because the discovery process is far from being completely automated (Valdes Perez, 1995). One area where the human component has been vital is in guiding the discovery system based on background knowledge. In this paper we augment a standard inductive learning program by connecting data items to background knowledge represented as inheritance networks with role links and a limited ....
Valdes-Perez, R., (1995). Some Recent HumanComputer Discoveries in Science and What Accounts for Them. AI Magazine, 16(3), pp. 37--44.
Augmenting Medical Databases with Domain Knowledge - Aronis, Buchanan, Lee (1996) (2 citations) (Correct)
....data, but explicit in the representation of domain knowledge. Programs for automated, inductive discovery have been shown to be effective in discovering patterns from data. Some discoveries have been made that are important enough to be published in the literature of the scientific subject domain [9]. Although induction programs by themselves can make interesting discoveries, we focus here on removing the severe restriction that a learning program always works within a small, fixed, semantic bias. We illustrate these points in the domain of plant exposures, with the RL program [2] extended ....
Valdes-Perez, R., (1995). Some Recent Human-Computer Discoveries in Science and What Accounts for Them. AI Magazine, 16(3), pp. 37--44.
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