### TABLE II RESULTS FOR ON-LINE IMAGE CLASSIFICATION

2007

Cited by 1

### Table 1. Facets of on-line synchronous learning model

2005

"... In PAGE 2: ... In this regard, we propose an on-line synchronous learning model that classifies various possible synchronous learning modes. Table1 describes five distinct dimensions of the model: role, participant, venue, interaction and delivery. A typical scenario in traditional classroom is like this: Teachers and students go to a specific classroom at the same period of time.... In PAGE 3: ... Teachers may also employ a combination of these two modes to provide better flexibility, hence using hybrid mode. Table1 summarizes the facets of synchronous learning model. Each cell of table 1 represents one synchronous learning mode.... ..."

Cited by 1

### Table 1: Lower bounds on the competitive ratio of on-line algorithms, depending on the platform type and on the objective function.

2005

"... In PAGE 5: ...erformance ratio will be, say, 1.1 while in another one (task T on P3) it will be, say, 1.2. Clearly, the minimum of the performance ratios over all execution scenarios is the desired bound on the competitive ratio of the algorithm: no algorithm can do better than this bound! Because we have three platform types (communication-homogeneous, computation-homoge- neous, fully heterogeneous) and three objective functions (makespan, max-flow, sum-flow), we have nine bounds to establish. Table1 summarizes the results, and shows the influence on the platform type on the difficulty of the problem. As expected, mixing both sources of heterogeneity (i.... In PAGE 20: ... We enforce the one-port model, and we study the impact of heterogeneity on the performance of scheduling algorithms. The major contribution of this paper lies on the theoretical side, and is well summarized by Table1 . We have provided a comprehensive set of lower bounds for the competitive ratio of any deterministic scheduling algorithm, for each source of heterogeneity and for each target objective function.... ..."

### Table 1: Lower bounds on the competitive ratio of on-line algorithms, depending on the platform type and on the objective function.

in The`me NUM

"... In PAGE 6: ....1 while in another one (task T on P3) it will be, say, 1.2. Clearly, the minimum of the performance ratios over all execution scenarios is the desired bound on the competitive ratio of the algorithm: no algorithm can do better than this bound! Because we have three platform types (communication-homogeneous, computation-ho- mogeneous, fully heterogeneous) and three objective functions (makespan, max-flow, sum- flow), we have nine bounds to establish. Table1 summarizes the results, and shows the influence on the platform type on the difficulty of the problem. As expected, mixing both sources of heterogeneity (i.... In PAGE 26: ... We enforce the one-port model, and we study the impact of heterogeneity on the performance of scheduling algorithms. The major contribution of this paper lies on the theoretical side, and is well summarized by Table1 . We have provided a comprehensive set of lower bounds for the competitive ratio of any deterministic scheduling algorithm, for each source of heterogeneity and for each target objective function.... ..."

### Table 2: Compared performance of explicit and on-line garbage modeling in a vocabulary-dependent isolated recognition task.

1996

"... In PAGE 3: ... In these systems, we previously used a garbage model constructed as a combination of context independent subword HMMs trained using only the keyword training set, as we presented in [6]. Table2 presents the comparative results of applying explicit garbage modeling and on-line rejection Experiment 2 Experiment 3 Experiment 1 Discrimination error 0.1 Threshold System Rejection Level SER SRR FAR 1stcan 3rdcan Baseline not applied 9.... ..."

Cited by 8

### Table 2. NTRS contents and usage. Service Reports on-line Citations on-line Reports served

### Table 2. NTRS contents and usage. Service Reports on-line Citations on-line Reports served

### Table 3: On-line coupled MetM - CTMs

2007

"... In PAGE 13: ... stratos. ozone tracer) Table3 shows some characteristics of the above mentioned on-line coupled MetM and CTM systems. However, it is necessary to mention, that many of the above mentioned on-line models were not build for mesometeorological scale, and they (e.... ..."

### Table 1. Web vs. Original On-line Service Model

1997

Cited by 3