Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 368 for “"discriminative"”.
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Gabapentin Discriminative Stimulus Effects
… drug effects of gabapentin when established as a discriminative stimulus as well as GABA<sub>A</sub> and NMDA ligands. Ten female Sprague-Dawley rats were trained on a drug discrimination paradigm using a 300.0 mg/kg gabapentin training dose as a discriminative stimulus. Gabapentin was …
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Discriminative Stimulus Effects of Gabapentin
<p>The present study sought to evaluate the discriminative stimulus effects of the anticonvulsant gabapentin in rats trained to discriminate 30.0 mg/kg gabapentin from vehicle in a two-lever drug discrimination task. All of the ten rats tested were able to establish gabapentin as an interoceptive …
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Discriminative, generative, and imitative learning
… paradigms in machine learning: generative, discriminative and imitative learning. A generative probabilistic distribution is a principled way to model many machine learning and machine perception problems. Therein, one provides domain specific knowledge in terms of structure and parameter …
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Advances in discriminative dependency parsing
… in dependency parsing has highlighted the discriminative structured prediction framework (McDonald et al., 2005a; Carreras, 2007; Suzuki et al., 2009), which is characterized by two advantages: first, the availability of powerful discriminative learning algorithms like log-linear and …
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Discriminative Classification Models for Internet Security
… received from the sending server. In general, discriminative classification methods learn to distinguish positive from negative entities. Each decision for a label may be based on features of the entity and related entities. When labels of related entities have strong interdependencies---as can …
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Aspects of generative and discriminative classifiers
… under the new terminology of generative and discriminative classifiers, research interest in classical statistical approaches to discriminant analysis has re-emerged in the machine learning community. In discriminant analysis, observations with features $\mathbf{x}$ measured are classified …
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Learning discriminative models with incomplete data
… to incorrect labels in the training data. The discriminative paradigm of classification aims to model the classification boundary directly by conditioning on the data points; however, discriminative models cannot easily handle incompleteness since the distribution of the observations is never …
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Discriminative Stimulus Effects of Putative Antipsychotic Drugs
<p>This study attempted to further explore the discriminative stimulus properties of antipsychotic drugs, by establishing the typical antipsychotic drug chlorpromazine, and the atypical antipsychotic drug clozapine as discriminative stimulus in two different groups of rats. The rats trained to …
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Mining time-series data using discriminative subsequences
… A shapelet is a time-series subsequence that is discriminative of the class of the original series. We use a heterogeneous ensemble classifier on the transformed data. The accuracy of our method is significantly better than the time-series classification benchmark (1-nearest-neighbour with …
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Discriminative methods for statistical spoken dialogue systems
… potentially be useful. This thesis presents how discriminative methods can overcome these problems in Spoken Language Understanding (SLU) and Dialogue State Tracking (DST). A robust method for SLU is proposed, based on features extracted from the full posterior distribution of recognition …
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Discriminative stimulus properties of 3-substituent rimonabant analogs
… These analogs also failed to elicit THC-like discriminative stimulus effects, nor did they antagonize THC’s discriminative stimulus in mice discriminating 5.6 mg/kg THC from vehicle. Finally, mice were trained to discriminate 5.6 mg/kg O-6629 from vehicle. O-6658 produced full substitution for …
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Unified Discriminative Subspace Learning for Multimodality Image Analysis
To demonstrate the effectiveness of the framework, an expert model of the query-driven locally adaptive (QDLA) method and four new subspace learning algorithms corresponding to different learning-locality criteria are presented. These four algorithms are locally embedded analysis (LEA), …
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Application of Prior Information to Discriminative Feature Learning
Learning discriminative feature representations has attracted a great deal of attention since it is a critical step to facilitate the subsequent classification, retrieval and recommendation tasks. In this dissertation, besides incorporating prior knowledge about image labels into the image …
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Generative and Discriminative Models in Phase Transition Prediction
… understanding physical systems. Generative and discriminative models offer promising yet distinct approaches. Considering varying knowledge levels of the system, accessible data amounts, and computation resources of the experiments, these methods exhibit different accuracy and efficiency. This …
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Towards bridging generative and discriminative learning for visual perception
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-05-01
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Discriminative training of hidden Markov Models for gesture recognition
… criterion and cannot perform as well as a discriminative classifier. This study employs minimum classification error training to produce a discriminative HMM classifier. The classifier is then applied to an isolated gesture recognition problem, using skeletal features. The Montalbano …
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Discriminative and adaptive training for robust speech recognition and understanding
… of deep learning. To achieve robust ASU, two discriminative training objectives are proposed for keyword spotting and topic classification: (1) To accurately recognize the semantically important keywords, the non-uniform error cost minimum classification error training of deep neural network …
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