Titlebook: Data Mining and Knowledge Discovery Approaches Based on Rule Induction Techniques; Evangelos Triantaphyllou,Giovanni Felici Book 2006 Spri

合適

真實的你

David T. Rubin,Abraham H. Dachmanhod derives from given training data certain minimum cost satisfiability problems, solves these problems, and deduces from the solutions the desired logic formulas. There are at least two ways in which the results may be employed. First, one may use the logic formulas directly as rules in applicatio

Anemia

Preoperative Staging of Rectal Cancer order to obtain a more essential and compact representation of the available information. The selected subset has to be small in size and must retain the information that is most useful for the specific application. The role of Feature Selection is particularly important when computationally expens

ORE

P. Earnshaw,A. Busuttil,A. Fergusonormula constructors, so that records can be processed that contain rational number and/or nominal values. A nominal value is an element or subset of a given finite set. In such cases, the rational numbers or nominal values must first be transformed to . values before the method may be applied. This

遺產(chǎn)

先驅(qū)

creditor

Yujiro Nishioka,Junichi Shindohtop-down induction of decision trees is one of the most popular techniques for inducing such classification models. Most of the research in decision tree induction has focused on single attribute trees, but in this chapter we review multi-attribute decision trees induction and discuss how such metho

forbid

One Operation for Double Pathologyl organisms. This ability relies heavily on the quality of the raw information available about the target system. In reality, these raw information/data may contain uncertainty and fuzziness, that is, it may be imprecise or incomplete. A number of techniques, such as the Dempster-Shafer theory of be

LATE

Blind Pouch Syndrome After Bowel Resectionses on discovering accurate, comprehensible rules. In this chapter we also take these two criteria into account, but we go beyond them in the sense that we aim at discovering rules that are interesting (surprising) for the user. Hence, the search for rules is guided by a rule-evaluation function tha

PAC