The Experts below are selected from a list of 119370 Experts worldwide ranked by ideXlab platform
Jack Sklansky - One of the best experts on this subject based on the ideXlab platform.
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Robust classifiers by mixed adaptation
IEEE Transactions on Pattern Analysis and Machine Intelligence, 1991Co-Authors: D. Gutfinger, Jack SklanskyAbstract:The production of robust classifiers by combining supervised Training with unsupervised Training is discussed. A supervised Training Phase exploits statistically scene invariant labeled data to produce an initial classifier. This is followed by an unsupervised Training Phase that exploits clustering properties of unlabeled data. This two-Phase process is termed mixed adaptation. A probabilistic model supporting this technique is presented along with examples illustrating mixed adaptation. These examples include the detection of unspecified dotted curves in dotted noise and the detection and classification of vehicles in cinematic sequences of infrared imagery. >
Kornél Németh - One of the best experts on this subject based on the ideXlab platform.
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Stimulus dependence and cross-modal interference in sequence learning.
Quarterly journal of experimental psychology (2006), 2016Co-Authors: Ferenc Kemény, Kornél NémethAbstract:ABSTRACTA central issue in sequence learning is whether learning operates on stimulus-independent abstract elements, or whether surface features are integrated, resulting in stimulus-dependent learning. Using the serial reaction-time (SRT) task, we test whether a previously presented sequence is transferrable from one domain to another. Contrary to previous artificial grammar learning studies, there is mapping between pre- and posttransfer stimuli, but contrary to previous SRT studies mapping is not obvious. In the pre-transfer Training Phase, participants face a dot-counting task in which the location of the dots follows a predefined sequence. In the test Phase, participants face an auditory SRT task in which the spatial organization of the response locations is either the same as spatial sequence in the Training Phase, or not. Sequence learning is compared to two control conditions: one with a non-sequential random-dot counting in the Training Phase, and one with no Training Phase. Results show that seq...
R. De Beaurepaire - One of the best experts on this subject based on the ideXlab platform.
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Calcitonin microinjection into the periaqueductal gray impairs contextual fear conditioning in the rat.
Neuroscience letters, 1999Co-Authors: Rachida Aboufatima, Abderrhaman Chait, Abderrahim Dalal, R. De BeaurepaireAbstract:We have previously proposed that behavioral alterations induced by salmon calcitonin in the rat provide an animal model of depression. As depression is characterized by context-related anxiety, behavioral inhibition and alterations in memory processing, we tested the effects of microinjections of salmon calcitonin into the periaqueductal gray matter (PAG) on contextual fear conditioning in the rat. In a first experiment, calcitonin or saline were microinjected into the PAG before the Training Phase and before the testing Phase of a conditional fear testing procedure. In a second experiment, calcitonin or saline were injected before and immediately after the Training Phase. When given before the Training Phase, calcitonin had no effects on immediate postshock freezing but produced significant deficits in contextual freezing (24 h after footshock) in comparison with controls. When given immediately after the footshocks, calcitonin impaired contextual fear. These results suggest that calcitonin receptor stimulation in the PAG can alter the acquisition and consolidation of contextual fear behavior processes.
Craig Twist - One of the best experts on this subject based on the ideXlab platform.
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The influence of` preseason Training Phase and Training load on body composition and its relationship with physical qualities in professional junior rugby league players.
Journal of sports sciences, 2018Co-Authors: Nick Dobbin, Adrian Gardner, Matthew Daniels, Craig TwistAbstract:This study investigated changes in body composition in relation to Training load determined using RPE and duration (sRPE), and its relationship with physical qualities over a preseason period. Sixteen professional academy players (age = 17.2 ± 0.7 years; stature = 179.9 ± 4.9 cm; body mass = 88.5 ± 10.1 kg) participated in the study. Body composition was assessed before and after each Training Phase and physical qualities assessed at the start and end of preseason. Across the whole preseason period, skinfold thickness, body fat percentage and fat mass were most likely lower (ES = -0.73 to -1.00), and fat free mass and lean mass were likely to most likely higher (ES = 0.31 to 0.40). Results indicated that the magnitude of change appeared Phase-dependent (ES = -0.05 to -0.85) and demonstrated large individual variability. Changes in physical qualities ranged from unclear to most likely (ES = -0.50 to 0.64). Small to moderate correlations were observed between changes in body composition, and TL with changes in physical qualities. This study suggests Training Phase and TL can influence a player's body composition; that large inter-participant variability exists; and that body composition and TL are related to the change in physical qualities.
Kieran Mclaughlin - One of the best experts on this subject based on the ideXlab platform.
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svm Training Phase reduction using dataset feature filtering for malware detection
IEEE Transactions on Information Forensics and Security, 2013Co-Authors: Philip Okane, Sakir Sezer, Kieran MclaughlinAbstract:N-gram analysis is an approach that investigates the structure of a program using bytes, characters, or text strings. A key issue with N-gram analysis is feature selection amidst the explosion of features that occurs when N is increased. The experiments within this paper represent programs as operational code (opcode) density histograms gained through dynamic analysis. A support vector machine is used to create a reference model, which is used to evaluate two methods of feature reduction, which are “area of intersect” and “subspace analysis using eigenvectors.” The findings show that the relationships between features are complex and simple statistics filtering approaches do not provide a viable approach. However, eigenvector subspace analysis produces a suitable filter.
