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Table 6.4 shows the confusion matrices obtained for the test set of the 5 data sets, both for our method with AL BP BCBMBD and for the C4.5 program. The confusion matrices show how many objects have been correctly classified as belonging to class BD, how many objects have been wrongly classified as belonging to class BE and so on. We see from the results that the two methods perform approximately equally, with a small advantage for our method.

in Induction of Decision Trees From Partially Classified Data Using Belief Functions
by Marte Skarstein Bjanger, Fakultet For Fysikk, Informatikk Og Matematikk, Fag Datateknikk Kunnskapssystemer

TABLE I SPEEDS AND ERROR RATES ACHIEVED BY DIFFERENT METHODS ON THE MNIST DATASET. RESULTS USING BRUTE-FORCE SEARCH WERE OBTAINED BY CHOOSING A RANDOM SUBSET OF THE DATABASE, INCLUDING AS MANY OBJECTS AS THE NUMBER SHOWN IN THE DISTANCES PER QUERY OBJECT COLUMN.

in BoostMap: An Embedding Method for Efficient Nearest Neighbor Retrieval
by Vassilis Athitsos, Jonathan Alon, Stan Sclaroff, George Kollios

TABLE I SPEEDS AND ERROR RATES ACHIEVED BY DIFFERENT METHODS ON THE MNIST DATASET. RESULTS USING BRUTE-FORCE SEARCH WERE OBTAINED BY CHOOSING A RANDOM SUBSET OF THE DATABASE, INCLUDING AS MANY OBJECTS AS THE NUMBER SHOWN IN THE DISTANCES PER QUERY OBJECT COLUMN.

in BoostMap: An Embedding Method for Efficient Nearest Neighbor Retrieval
by Vassilis Athitsos, Jonathan Alon, Stan Sclaroff, George Kollios

Tables Secondly, systems have user interfaces and user interfaces contain objects or ridgets. Their number drives the effort required to test those user interfaces. The more objects the tester has to manipulate, the higher the test effort. As is the case with the database tables, it is easier to test many user interfaces with few objects than a few interfaces with many objects. Thus, the user interface testability is the relationship of user interfaces to objects contained therein, whereby it is assumed that each user interface has at least two objects, giving the metric

in Editors
by Alain Abran, Manfred Bundschuh, Reiner Dumke, Christof Ebert, Horst Zuse 1992
Cited by 1

Table 11: User Preferences

in Abstract
by Fabio Massacci, John Mylopoulos, Nicola Zannone, Fabio Massacci, John Mylopoulos, Nicola Zannone 2006
"... In PAGE 16: ...e. the privacy penalty, is speci ed by the customer in his preferences (column 1 of Table11 ). The algorithm extracts from the queue PQ the node t with minimum priority ct which is assumed to be the privacy penalty of the minimal decomposition path from ? to t.... In PAGE 16: ... The output of the MinimumCost algorithm (DISCLOSE and NEEDED) allows to build the mini- mum cost decomposition path, including the list of data items required by the corresponding process. Example 5 Table11 reports the value of data items and delegation steps that Mississippi uses to initialize the business process. It prefers to deliver books using a delivery company because this method is safer and faster.... ..."

Table 2: Look-up table used by the MODS to determine how many object instances can be placed in a test image.

in unknown title
by unknown authors
"... In PAGE 7: ...Table2 ). For example, if the maximum dimension of an object is 90 then the MODS will place 9 instances of the object in each test image.... ..."

Table 5. User Preferences

in Minimal Disclosure in Hierarchical Hippocratic Databases with Delegation
by Fabio Massacci, John Mylopoulos, Nicola Zannone 2005
"... In PAGE 10: ... Example 4. Defining default preferences, Mississippi gives a value on data items and delegation steps ( Table5 ). It prefers to deliver books by using a delivery company because this method is more secure and faster.... ..."
Cited by 1

Table 5: User Preferences

in Abstract
by Fabio Massacci, John Mylopoulos, Nicola Zannone, Fabio Massacci, John Mylopoulos, Nicola Zannone 2005
"... In PAGE 11: ... Procedure ScanMC aims at analyzing OR-edges ht; xi: if the ingoing node x is not already visited, the procedure inserts it in the priority queue; otherwise, the penalty of x is updated if and only if edge ht; xi improves the old penalty associated with x. Example 4 De ning default preferences, Mississippi gives a value on data items and delegation steps ( Table5 ). It prefers to deliver books by using a delivery company because this method is more secure and faster.... ..."

Table 2. Average MIR accuracy and ef ciency of query integration and conventional methods (Num = 50) Method

in CONTENT-BASED MUSIC RETRIEVAL USING QUERY INTEGRATION FOR USERS WITH DIVERSE PREFERENCES
by Keiichiro Hoashi, Hiromi Ishizaki, Kazunori Matsumoto, Fumiaki Sugaya
"... In PAGE 3: ... 5.4 Results Comparison of the MIR accuracy of the proposed query integration method (QI) and the conventional method (Conv) is shown in Table2 , where the average ratio of song cate- gories in all MIR results when Num = 50 is listed. For additional comparison, we also present the results of ex- periments when no query integration is conducted (No- QI), i.... In PAGE 3: ... Furthermore, the average CalcRate of all experiments are also written in this Table. Results in Table2 indicate that all methods have ac- curately retrieved songs that t user preferences, since the ratio of songs in Cg in the MIR results all exceed the over- all ratio of songs in Cg (=37.0%, from Table 1).... In PAGE 4: ... There- fore, a method which can determine an optimal threshold for query integration, based on the features of the songs in the sample set, is assumed to be necessary for further improvement of MIR accuracy. 6 CLUSTER SELECTION MIR While the previous experiment has proved the effective- ness of query integration, it is also true that the method increases the computational cost of the MIR process, as indicated by the high CalcRate values in Table2 . In order to improve MIR ef ciency, we propose a selective MIR method, which utilizes the clustering results of the songs in the collection.... ..."

Table 10: Time vs. Budget preference for users

in This is to certify that the Thesis titled “Market Economy Based Resource
by Sai Rahul Reddy P, Dr. Arobinda Gupta, Dr. Arobinda Gupta 2006
"... In PAGE 9: ...imeOptimized policy ........................................................................................47 Table10... ..."
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