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Table 2: Scale factor s(K) and respective standard deviation. 5 bits, base=2. Using 50 points and sam- ple repetitions per point.

in Extrema Propagation: Fast Distributed Estimation of Sums and Network Sizes Abstract
by Carlos Baquero, Paulo Sérgio, Almeida Raquel Menezes

Table 9: Results on Senseval-3 English lexical sam- ple task

in Improving Word Sense Disambiguation Using Topic Features
by Jun Fu Cai, Wee Sun Lee

Table 9: Results on Senseval-3 English lexical sam- ple task

in Improving Word Sense Disambiguation Using Topic Features
by Jun Fu Cai, Wee Sun Lee

Table 3. The learn routine accepts the number of trials it is al- lowed and returns its guess at the optimal policy. It relies on four external routines: pick sample which selects a policy to sam- ple given the data and the current best guess, sample as shown in table 1, add data which adds the new data point to the data collected so far, and optimize which performs some form of optimization on the proxy evaluation function.

in Learning from Scarce Experience
by Leonid Peshkin, Christian Shelton 2002
"... In PAGE 4: ... Any policy search algorithm can now be combined with this proxy environment to learn from scarce experience. Table3 shows a general reinforcement learning algorithm family using the proxy. The definitions of pick sample, add data, and optimize are crucial to the behavior of the algorithm.... ..."
Cited by 7

Table 3. The learn routine accepts the number of trials it is al- lowed and returns its guess at the optimal policy. It relies on four external routines: pick sample which selects a policy to sam- ple given the data and the current best guess, sample as shown in table 1, add data which adds the new data point to the data collected so far, and optimize which performs some form of optimization on the proxy evaluation function.

in Learning from scarce experience
by Leonid Peshkin, Christian R. Shelton 2002
"... In PAGE 4: ... Any policy search algorithm can now be combined with this proxy environment to learn from scarce experience. Table3 shows a general reinforcement learning algorithm family using the proxy. The definitions of pick sample, add data, and optimize are crucial to the behavior of the algorithm.... ..."
Cited by 7

Table 2: Results on Senseval-2 English lexical sam- ple using different Bayesian network approaches.

in Improving Word Sense Disambiguation Using Topic Features
by Jun Fu Cai, Wee Sun Lee
"... In PAGE 5: ... As the dataset is used extensively for this purpose, only the Senseval-3 lex- ical sample task is used for evaluation. Selecting Bayesian Network The best achievable result, using the three different Bayesian network approaches, when validating on Senseval-2 test data is shown in Table2 . The parameters that are used... ..."

Table 8: Queueing Network, finite buf., deter. service, sam- ple 109,000.

in Generol Conference (Part A) Estimating Traffic Intensities at Different Nodes in Networks via a Probing Stream
by Vinod Sharma

Table 2: Results on Senseval-2 English lexical sam- ple using different Bayesian network approaches.

in Improving Word Sense Disambiguation Using Topic Features
by Jun Fu Cai, Wee Sun Lee
"... In PAGE 5: ... As the dataset is used extensively for this purpose, only the Senseval-3 lex- ical sample task is used for evaluation. Selecting Bayesian Network The best achievable result, using the three different Bayesian network approaches, when validating on Senseval-2 test data is shown in Table2 . The parameters that are used... ..."

Table 2. Pre-compensation frequency and power distribution (normalized to nominal) due to systematic WID variation for the sam- ple floorplan shown in Figure 2.

in Impact of process variations on multicore performance symmetry
by Eric Humenay, David Tarjan, Kevin Skadron 2007
"... In PAGE 4: ... Pre-compensation frequency and power distribution (normalized to nominal) due to systematic WID variation for the sam- ple floorplan shown in Figure 2. ing to rows of cores, as shown in Table2 . Within a row, the frequency variation is minimal.... ..."
Cited by 2

Ta ble A1: Sam ple Statistics of Key Variables for Students w ith Negativ e Respsonses to Additional Hom ew ork

in Motivation and achievement
by Ozkan Eren, Daniel J. Henderson, Kevin Grier, Qi Li, Essie Maasoumi, Daniel Millimet, Jeff Racineforhelpfulcommentswhichledtoanimprovedversionofthispaperaswellasparticipantsofthe 1990
Cited by 1
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