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Table-1 Aspects covered under General evaluation of site selection

in Site Selection Criteria For Hazardous Waste
by Treatment Storage And, V. Rama Krishna, B. V. Babu

Table 6: Generalizations Found by C4.5 for Navajo Aspect.

in 1 A Computational Analysis of Navajo Verb Stems
by David Eddington, Jordan Lachler

Table 3: Approximate areas under the ROC curves, along with 95% con dence intervals, for naive Bayes and Budds when generalizing across aspect (A), generalizing across location (L), and general- izing within an image (W). The labels `nadir apos; and `oblique apos; indicate the testing condition.

in Improving Rooftop Detection with Interactive Visual Learning
by Kamal M. Ali, Pat Langley, Marcus A. Maloof, Stephanie Sage, Thomas Binford
"... In PAGE 7: ... One obvious conclusion is that the nadir images ap- pear to pose an easier problem than the oblique im- ages, since the curves for testing on nadir candidates are generally higher than those for testing on data from oblique images. For example, Table3 shows that naive Bayes generates a curve with an area of 0.878 for the nadir images, but produces one with an area of 0.... ..."

Table 3: Approximate areas under the ROC curves, along with 95% con dence intervals, for naive Bayes and Budds when generalizing across aspect (A), generalizing across location (L), and general- izing within an image (W). The labels `nadir apos; and `oblique apos; indicate the testing condition.

in Experimental Studies of Interactive Visual Learning
by Kamal Ali, Pat Langley, Marcus A. Maloof, Thomas O. Binford
"... In PAGE 7: ... One obvious conclusion is that the nadir images ap- pear to pose an easier problem than the oblique im- ages, since the curves for testing on nadir candidates are generally higher than those for testing on data from oblique images. For example, Table3 shows that naive Bayes generates a curve with an area of 0.878 for the nadir images, but produces one with an area of 0.... ..."

Table 6: Aspects of programming models

in Wide-Area Parallel Programming using the Remote Method Invocation Model
by Rob van Nieuwpoort, Jason Maassen, Henri E. Bal, Thilo Kielmann, Ronald Veldema 2000
"... In PAGE 18: ...Comparison of the models To compare the suitability of the models for wide-area parallel computing, we study three impor- tant aspects. Table6 summarizes this comparison. In general, we assume that the underlying pro- gramming system (like Manta) exposes the physical distribution of the clustered wide-area system.... ..."
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Table 6: Aspects of programming models

in Wide-Area Parallel Programming using the Remote Method Invocation Model
by Rob Van Nieuwpoort, Jason Maassen, Henri E. Bal, Thilo Kielmann, Ronald Veldema
"... In PAGE 18: ...Comparison of the models To compare the suitability of the models for wide-area parallel computing, we study three impor- tant aspects. Table6 summarizes this comparison. In general, we assume that the underlying pro- gramming system (like Manta) exposes the physical distribution of the clustered wide-area system.... ..."

Table 6: Aspects of programming models

in Wide-Area Parallel Programming using the Remote Method Invocation Model
by Rob van Nieuwpoort, Jason Maassen, Henri E. Bal, Thilo Kielmann, Ronald Veldema
"... In PAGE 17: ...2 Comparison of the models To compare the suitability of the models for wide-area parallel computing, we study three impor- tant aspects. Table6 summarizes this comparison. In general, we assume that the underlying pro- gramming system (like Manta) exposes the physical distribution of the clustered wide-area system.... ..."

Table 1: Comparison of approaches to modeling aspect mechanisms

in General Terms
by Sergei Kojarski, David H. Lorenz
"... In PAGE 2: ... is useful precisely because it does some crosscutting thing very dif- ferent, abstracting over several aspect mechanisms is difficult and often results in a fine-grained model which would likely need to be further extended to fit future aspect mechanisms.2 We distinguish between two general approaches to modeling as- pect mechanisms, namely, semantical and conceptual ( Table1 , two left columns). Existing semantical models either explain a specific AspectJ-like Pointcut and Advice (PA) mechanism or generalize a PA model.... ..."

Table 2: High probability words for each aspect.

in Mixed Membership Models of Scientific Publications
by Elena Erosheva Stephen, Stephen Fienberg, John Lafferty 2004
"... In PAGE 7: ...nterpretation for the eight aspect model. Therefore we report only the results of the eight aspect model here. To see whether there are certain contexts that correspond to the aspects, we examine the most common words in the estimated multinomial distributions. In Table2 we report the first 15 of the high probability words for each aspect, filtering out so called stop words , words that are generally common in English. An alternative way would be to discard the words from the stop list before fitting the model.... ..."
Cited by 8

Table 4: The similar words of aspects

in Contextual word similarity and estimation from sparse data
by Ido Dagan, Shaul Marcus, Shaul Markovitch 1993
"... In PAGE 16: ... Using these weights, we get the following weighted average as the general de#0Cnition of similarity: sim#28w 1 ;w 2 #29= #288#29 P w2lexicon sim L #28w 1 ;w 2 ;w#29#01W L #28w 1 ;w 2 ;w#29+sim R #28w 1 ;w 2 ;w#29#01W R #28w 1 ;w 2 ;w#29 P w2lexicon W L #28w 1 ;w 2 ;w#29+W R #28w 1 ;w 2 ;w#29 = P w2lexicon min#28I#28w; w 1 #29;I#28w; w 2 #29#29 + min#28I#28w 1 ;w#29;I#28w 2 ;w#29#29 P w2lexicon max#28I#28w; w 1 #29;I#28w; w 2 #29#29 + max#28I#28w 1 ;w#29;I#28w 2 ;w#29#29 The values produced by this metric haveanintuitiveinterpretation, as denoting a#5Ctypical quot; ratio between the twomutual information values of each of the twowords with another third word. Table4 lists the six most similar words to the word `aspects apos; according to this metric, based on our corpus. Out of the six words, #0Cve can be considered as similar to `aspects apos; according to our own intuition.... ..."
Cited by 76
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