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Table 1. Type rules for A .
2004
"... In PAGE 12: ... For a tuple ~ x, we de ne ch(~ x) : f~ xg ! f~ xg as ch( ) = fg, and if len(~ x) = n, ch(~ x)(xi) = xi+1 for 1 i lt; n and ch(~ x)(xn) = ?. a0 The type rules are shown in Table1 . Rules NIL and MSG are obvious.... In PAGE 20: ... Speci cally, the type system of A reduces the number of observers that can be used to test actor con gurations. For example, the following two processes are distinguishable in asynchronous - calculus, but equivalent in A : P = ( x)(x(z):0jxxjyx) Q = ( x)(x(z):0jyx) The observer O = y(z):z(w): can distinguish P and Q in asynchronous - calculus, but is not a valid A term as it violates the freshness property (ACT rule of Table1 ). In fact, no A term can distinguish P and Q, because the message xx is not observable.... ..."
Cited by 2
Table 3: Selection rules for a comparison
1999
Cited by 13
Table 2. Five rules in a C4.5 tree derived from a prostate disease gene expression profiling data
2003
"... In PAGE 4: ... We name the rules 1, 2, 3, 4 and 5 from the left side to right. Their respective coverage and number of features contained are listed in Table2 . Rule 1 is the most significant rule: it has a 94% coverage over the tumor class.... ..."
Table 1: Mining discriminant rules: A comparison of researchgrants in twoprovinces
in Abstract
"... In PAGE 19: ...here A.grant code = G.grant code and A.OrgID = O.OrgID and A.disc code = `Computer apos; related to disc code, grant category, count(*)% The execution of this data mining query generates Table1 which presents the di erences between the two provinces in terms of disc code, grant category and the number of the research grants. The column support% represents the number of researchgrants in this category vs.... ..."
Table 5. Additional rewrite rules (a 6 = b)
"... In PAGE 7: ...ormal forms. This is due to the fact that e.g. axiom A9 is sometimes needed in the opposite direction. So, we complete the term rewriting system by adding the rewrite rules in Table5 . Note that each new rewrite rule is derivable from the axioms for the delayed choice.... ..."
Table 1 Pedagogical planning rules for a content planner
1996
"... In PAGE 4: ... The pedagogical philosophy encoded in the PPR is captured in goal generation rules, plan generation rules and plan monitoring rules which correspond to the three phases of content planning. An example of the PPR can be found in Table1 . The rules shown here encode a tutor-style instructional strategy where the system controls the decision of the next concept on which to focus.... ..."
Cited by 8
Table 1: Mining discriminant rules: A comparison of research grants in two provinces
in Abstract
"... In PAGE 19: ...here A.grant code = G.grant code and A.OrgID = O.OrgID and A.disc code = `Computer apos; related to disc code, grant category, count#28*#29#25 The execution of this data mining query generates Table1 which presents the di#0Berences between the two provinces in terms of disc code, grant category and the number of the research grants. The column support#25 represents the number of research grants in this category vs.... ..."
TABLE 1 MINING DISCRIMINANT RULES: A COMPARISON OF RESEARCH GRANTS IN Two PROVINCES
Table 1: Mining discriminant rules: A comparison of research grants in two provinces 5 Data Classi cation Data classi cation is the process which nds the common properties among a set of objects in a database and classi es them into di erent classes, according to a classi cation model. To construct such a classi cation model, a sample database E is treated as the training set, in which each tuple consists of the same set of multiple attributes (or features) as the tuples in a large database W, and additionally, each tuple has a known class identity (label) associated with it. The objective of the classi cation is to rst analyze the training data and develop an accurate description or a model for each class using the features available in the data. Such class descriptions are then used to classify future test data in the database W or to develop a better description (called classi cation rules) for each class in the database. Applications of classi cation include medical diagnosis, performance prediction, selective marketing, to name a few. Data classi cation has been studied substantially in statistics, machine learning, neural net- works, and expert systems [82] and is an important theme in data mining [30].
1996
"... In PAGE 19: ...here A.grant code = G.grant code and A.OrgID = O.OrgID and A.disc code = `Computer apos; related to disc code, grant category, count(*)% The execution of this data mining query generates Table1 which presents the di erences between the two provinces in terms of disc code, grant category and the number of the research grants. The column support% represents the number of research grants in this category vs.... ..."
Cited by 289
Table 2: Bound on k for promotion to irreducibility for rulings on a quadric. hnb 2 3 4 5 6
2007
"... In PAGE 24: ...This preparatory work, summarized in Table2 , tells us that if rulings exist on the quadric, then they will be discovered by an irreducible decom- position of Q3 Y Z. The 9 polynomials for the flber product are: ^ F (x1; x2; x3; u; v) = 8 lt; : ^ f(x1; u; v) ^ f(x2; u; v) ^ f(x3; u; v) 9 = ; ; (12) We use ^ F to distinguish this system from the system F of 12 polynomials that would result from using f instead of ^ f.... ..."
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