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Table 1 Oculomotor data: individual subjects

in doi:10.1093/cercor/bhi011 Advance Access publication December 22, 2004 Canceling Planned Action: An fMRI Study
by Of Countermanding Saccades 2005

Table 1. Summary of oculomotor metrics and workload utility from various studies.

in Task Performance and Eye Activity: Predicting Behavior Relating to Cognitive Workload
by Y. Tsai, E. Viirre, C. Strychacz, B. Chase, T-p. Jung
"... In PAGE 25: ... Table1 . Summary of oculomotor metrics and workload utility from various studies.... ..."

Table 3. The contribution of each symptom to a subscale score is indicated by a 1 in the subscale columns (N - Nausea, O - Oculomotor, D - Disorientation).

in Assessing negative side effects in virtual environments. Unpublished thesis
by Robert C. Williges, John G. Casali, Thomas A. Dingus, Michael K. Mcgee, Michael K. Mcgee 1998
"... In PAGE 8: ...igure 17. Example test stimuli for the mental rotation test............................................29 Table3 .... In PAGE 42: ... Sixteen of the symptoms in the list are converted to three subscales by a series of arithmetic steps. Table3 shows which symptoms contribute to each subscale. The three subscales are N - Nausea, O - Oculomoter, and... In PAGE 43: ...The severity level of each symptom assigned by the participant (1 -None, 2 - Slight, 3 - Moderate, 4 - Severe) is assigned to the appropriate subscales as defined by Table3 above. The individual symptom responses are then summed to obtain subscale subtotals.... ..."
Cited by 1

Table 1) for its simplicity and its superior leakage behav- ior [2] compared to other structures [3, 4].

in unknown title
by unknown authors
"... In PAGE 1: ... Table1 : DHBT layer structure A 20nm InGaAs spacer layer is inserted under the 50 nm InGaAs base followed by two 20 nm quaternary layers to... ..."

Table 1.1: The di#0Berent IORs used by the Fault Tolerance Framework.

in Fault Tolerance for CORBA -- Version 1.0 - Initial RFP Submission
by Corba Version Initial, Sun Microsystems Inc, In Collaboration, Louise E. Moser, Roger J. Martin

Table 1: The ?7 most-cited female wwnllil~, Ior (he period 1965-197X.

in unknown title
by unknown authors 1982

Table 1: Over tting in the mixture of unigrams and pLSI models for the AP corpus. Similar behav- ior is observed in the nematode corpus (not reported).

in Latent dirichlet allocation
by David M. Blei, Andrew Y. Ng, Michael I. Jordan, John Lafferty 2003
"... In PAGE 19: ... Both the pLSI model and the mixture of unigrams suffer from serious over tting issues, though for different reasons. This phenomenon is illustrated in Table1 . In the mixture of unigrams model, over tting is a result of peaked posteriors in the training set; a phenomenon familiar in the super- vised setting, where this model is known as the naive Bayes model (Rennie, 2001).... ..."
Cited by 412

Table 1: Overfitting in the mixture of unigrams and pLSI models for the AP corpus. Similar behav- ior is observed in the nematode corpus (not reported).

in Latent Dirichlet Allocation
by David M. Blei, Andrew Y. Ng, Michael I. Jordan 2003
"... In PAGE 18: ...hat the average change in expected log likelihood is less than 0.001%. Both the pLSI model and the mixture of unigrams suffer from serious overfitting issues, though for different reasons. This phenomenon is illustrated in Table1 . In the mixture of unigrams model, overfitting is a result of peaked posteriors in the training set; a phenomenon familiar in the super- vised setting, where this model is known as the naive Bayes model (Rennie, 2001).... ..."
Cited by 412

Table 1: Overfitting in the mixture of unigrams and pLSI models for the AP corpus. Similar behav- ior is observed in the nematode corpus (not reported).

in Latent dirichlet allocation
by David M. Blei, Andrew Y. Ng, Michael I. Jordan, John Lafferty 2003
"... In PAGE 19: ... Both the pLSI model and the mixture of unigrams suffer from serious overfitting issues, though for different reasons. This phenomenon is illustrated in Table1 . In the mixture of unigrams model, overfitting is a result of peaked posteriors in the training set; a phenomenon familiar in the super- vised setting, where this model is known as the naive Bayes model (Rennie, 2001).... ..."
Cited by 412

Table 1: Overfitting in the mixture of unigrams and pLSI models for the AP corpus. Similar behav- ior is observed in the nematode corpus (not reported).

in Latent dirichlet allocation
by David M. Blei, Andrew Y. Ng, Michael I. Jordan, John Lafferty 2003
"... In PAGE 18: ... Both the pLSI model and the mixture of unigrams suffer from serious overfitting issues, though for different reasons. This phenomenon is illustrated in Table1 . In the mixture of unigrams model, overfitting is a result of peaked posteriors in the training set; a phenomenon familiar in the super- vised setting, where this model is known as the naive Bayes model (Rennie, 2001).... ..."
Cited by 412
Results 1 - 10 of 181