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"LEARNING AND PREDICTION OF RELATIONAL TIME SERIES" and the language of the book is English.


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  • Title: ➤  LEARNING AND PREDICTION OF RELATIONAL TIME SERIES
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  • Language: English

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  • Internet Archive ID: learningandpredi1094532907

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Prediction of events is fundamental to both human and artificial agents. The main problem with previous prediction techniques is that they cannot predict events that have never been experienced before. This dissertation addresses the problem of predicting such novelty by developing algorithms and computational models inspired from recent cognitive science theories conceptual blending theory and event segmentation theory. We were able to show that prediction accuracy for event or state prediction can be significantly improved using these methods. The main contribution of this dissertation is a new class of prediction techniques inspired by conceptual blending that improves prediction accuracy overall and has the ability to predict even events that have never been experienced before. We also show that event segmentation theory, when integrated with these techniques, results in greater computational efficiency. We implemented the new prediction techniques, and more traditional alternatives such as Markov and Bayesian techniques, and compared their prediction accuracy quantitatively for three domains a role-playing game, intrusion-system alerts, and event prediction of maritime paths in a discrete-event simulator. Other contributions include two new unification algorithms that improve over a nave one, and an exploration of ways to maintain a minimum-size knowledge base without affecting prediction accuracy.

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"LEARNING AND PREDICTION OF RELATIONAL TIME SERIES" is available for download from The Internet Archive in "texts" format, the size of the file-s is: 281.71 Mbs, and the file-s went public at Fri Apr 26 2019.

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  • Source: Internet Archive
  • Internet Archive Link: Archive.org page
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