Гауссовский Процесс

method ID: gaussianprocess

Related Papers

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Authors: nan

Publication: TIPSTER (2014)

DOI: nan

We propose a model for jointly predicting multiple emotions in natural language sen- tences. Our model is based on a low-rank coregionalisation approach, which com- bines a vector-valued Gaussian Process …...

34343

Reading Documents for Bayesian Online Change Point Detection

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Modeling non-stationary time-series data for making predictions is a challenging but important task. One of the key is- sues is to identify long-term changes accu- rately in time-varying data. Bayesian …...

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On-line Active Reward Learning for Policy Optimisation in Spoken Dialogue Systems

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The ability to compute an accurate re- ward function is essential for optimising a dialogue policy via reinforcement learn- ing. In real-world applications, using ex- plicit user feedback as the …...

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SSN_MLRG1 at SemEval-2017 Task 4: Sentiment Analysis in Twitter Using Multi-Kernel Gaussian Process Classifier

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Publication: TIPSTER (2017)

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AbstractThe SSN MLRG1 team for Semeval-2017 task 4 has applied Gaussian Process, with bag of words feature vectors and fixed rule multi-kernel learning, for sentiment analysis of tweets. Since tweets …...

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Finding Convincing Arguments Using Scalable Bayesian Preference Learning

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AbstractWe introduce a scalable Bayesian preference learning method for identifying convincing arguments in the absence of gold-standard ratings or rankings. In contrast to previous work, we avoid the need for …...

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Gaussian Process based Deep Dyna-Q approach for Dialogue Policy Learning

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Applying reinforcement learning to dialogue policy learning requires prohibitively large rounds of human-machine interactions. To improve the learning performance, the Deep Dyna-Q framework with a world model that imitates real …...

34026