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Table 1. ATMS labels for the academic rule base

in Efficient rule base verification using binary decision diagrams
by Christophe Mues, Jan Vanthienen 2004
"... In PAGE 3: ... At that point however, we observe that the second set is a superset of the first one, so it can as well be eliminated in the final label (cf. Table1 ). Accordingly, the rule path (r1, r2) does not contribute to the reasoning of (r4), and is thus found redundant.... ..."
Cited by 1

Table 4 Chi-Square Calculation for Number of Topics Raised by Students with Advisors by Academic Level

in unknown title
by unknown authors

Table 2: Rule elimination in second phase.

in Do not crawl in the DUST: different URLs with similar text
by Ziv Bar-yossef, Idit Keidar, Uri Schonfeld 2006
"... In PAGE 22: ... Figure 3) dramatically shortens this list. In all of our experiments, the latter phase has eliminated over 90% of the rules in the original list (see Table2 ). For example, in the largest log in the academic site, 26,899 likely rules were detected in the first phase, and only 2041 (8%) remained after the second; in a smaller log 10,848 rules were detected, of which only 354 (3%) were not eliminated.... ..."
Cited by 5

Table 1: Academic instances

in A Greedy Approach to Establish Singleton Arc Consistency
by unknown authors
"... In PAGE 5: ...0.66 0.68 0.7 0.72 0.74 Figure 1: mean cpu time on 50 random instances of class (100,20,0:05,t) at each value of t Next, we have dealt with the following academic instances: two chessboard coloration instances, denoted cc-20-2 and cc-20-3, involving quaternary constraints, two Golomb ruler instances, denoted gr-34-8 and gr-34- 9, involving binary and ternary instances, two prime queen attacking instances, denoted qa-5 and qa-6, involving only binary constraints. Table1 shows that SAC-3, and especially SAC-3+, have on some instances a much better behaviour than SAC-1 and SAC-SDS. Roughly speaking, it can explained by the fact that such instances have some regular structure.... ..."

Table 2: Academic Discipline (%)

in Contents
by Dr. Ir. Jeroen De Kloet 2002

Table IV. Academic engagement

in Teaching Electricity and Magnetism in electrical engineering curriculum: applied methods and trends Authors:
by unknown authors

Table XII. Academic results

in Teaching Electricity and Magnetism in electrical engineering curriculum: applied methods and trends Authors:
by unknown authors

Table 2 Academic Majors

in ABSTRACT SANDERS, CRAIG SIDNEY. Integrating Multimedia in Technology Education to Improve College Student Comprehension, Problem-solving Skills, and Attitudes Toward
by unknown authors

Table 2: Correlations with academic resilience

in Academic Resilience and the Four Cs: Confidence, Control, Composure, and Commitment
by Andrew J. Martin, Herbert W. Marsh, Andrew J. Martin, Herbert W. Marsh
"... In PAGE 7: ... Correlations Between Facets of the Wheel and Academic Resilience The first phase of analyses involved correlations between facets of the Student Motivation Wheel and academic resilience. Correlations appear in Table2 . Of the boosters, self-belief and persistence are most strongly related to academic resilience.... ..."

Table 2: Satisfaction of participants Foreign academics Dutch academics Dutch practitioners

in User Requirements Elicitation with GSS Design and Application of a Repeatable Process
by unknown authors
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