The Experts below are selected from a list of 321 Experts worldwide ranked by ideXlab platform
Ran Zhao - One of the best experts on this subject based on the ideXlab platform.
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Covariance-based Dissimilarity Measures Applied to Clustering Wide-sense Stationary Ergodic Processes
Machine Learning, 2019Co-Authors: Qidi Peng, Nan Rao, Ran ZhaoAbstract:We introduce a new unsupervised learning problem: clustering wide-sense stationary ergodic stochastic processes. A covariance-based dissimilarity measure together with asymptotically consistent algorithms is designed for clustering offline and online datasets, respectively. We also suggest a Formal Criterion on the efficiency of dissimilarity measures, and discuss an approach to improve the efficiency of our clustering algorithms, when they are applied to cluster particular type of processes, such as self-similar processes with wide-sense stationary ergodic increments. Clustering synthetic data and real-world data are provided as examples of applications.
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Covariance-based Dissimilarity Measures Applied to Clustering Wide-sense Stationary Ergodic Processes
arXiv: Machine Learning, 2018Co-Authors: Qidi Peng, Nan Rao, Ran ZhaoAbstract:We introduce a new unsupervised learning problem: clustering wide-sense stationary ergodic stochastic processes. A covariance-based dissimilarity measure together with asymptotically consistent algorithms is designed for clustering offline and online datasets, respectively. We also suggest a Formal Criterion on the efficiency of dissimilarity measures, and discuss of some approach to improve the efficiency of our clustering algorithms, when they are applied to cluster particular type of processes, such as self-similar processes with wide-sense stationary ergodic increments. Clustering synthetic data and real-world data are provided as examples of applications.
Qidi Peng - One of the best experts on this subject based on the ideXlab platform.
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Covariance-based Dissimilarity Measures Applied to Clustering Wide-sense Stationary Ergodic Processes
Machine Learning, 2019Co-Authors: Qidi Peng, Nan Rao, Ran ZhaoAbstract:We introduce a new unsupervised learning problem: clustering wide-sense stationary ergodic stochastic processes. A covariance-based dissimilarity measure together with asymptotically consistent algorithms is designed for clustering offline and online datasets, respectively. We also suggest a Formal Criterion on the efficiency of dissimilarity measures, and discuss an approach to improve the efficiency of our clustering algorithms, when they are applied to cluster particular type of processes, such as self-similar processes with wide-sense stationary ergodic increments. Clustering synthetic data and real-world data are provided as examples of applications.
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Covariance-based Dissimilarity Measures Applied to Clustering Wide-sense Stationary Ergodic Processes
arXiv: Machine Learning, 2018Co-Authors: Qidi Peng, Nan Rao, Ran ZhaoAbstract:We introduce a new unsupervised learning problem: clustering wide-sense stationary ergodic stochastic processes. A covariance-based dissimilarity measure together with asymptotically consistent algorithms is designed for clustering offline and online datasets, respectively. We also suggest a Formal Criterion on the efficiency of dissimilarity measures, and discuss of some approach to improve the efficiency of our clustering algorithms, when they are applied to cluster particular type of processes, such as self-similar processes with wide-sense stationary ergodic increments. Clustering synthetic data and real-world data are provided as examples of applications.
Hugo Jair Escalante - One of the best experts on this subject based on the ideXlab platform.
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von Neumann-Morgenstern and Savage Theorems for Causal Decision Making.
arXiv: Artificial Intelligence, 2019Co-Authors: Mauricio Gonzalez-soto, Luis Enrique Sucar, Hugo Jair EscalanteAbstract:Decision making under uncertain conditions has been well studied when uncertainty can only be considered at the associative level of information. The classical Theorems of von Neumann-Morgenstern and Savage provide a Formal Criterion for rationally making choices using associative information. We provide here a previous result from Pearl and show that it can be considered as a causal version of the von Neumann-Morgenstern Theorem; furthermore, we consider the case when the true causal mechanism that controls the environment is unknown to the decision maker and propose a causal version of the Savage Theorem. As applications, we argue how previous optimal action learning methods for causal environments fit within the Causal Savage Theorem we present thus showing the utility of our result in the justification and design of learning algorithms; furthermore, we define a Causal Nash Equilibria for a strategic game in a causal environment in terms of the preferences induced by our Causal Decision Making Theorem.
Nan Rao - One of the best experts on this subject based on the ideXlab platform.
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Covariance-based Dissimilarity Measures Applied to Clustering Wide-sense Stationary Ergodic Processes
Machine Learning, 2019Co-Authors: Qidi Peng, Nan Rao, Ran ZhaoAbstract:We introduce a new unsupervised learning problem: clustering wide-sense stationary ergodic stochastic processes. A covariance-based dissimilarity measure together with asymptotically consistent algorithms is designed for clustering offline and online datasets, respectively. We also suggest a Formal Criterion on the efficiency of dissimilarity measures, and discuss an approach to improve the efficiency of our clustering algorithms, when they are applied to cluster particular type of processes, such as self-similar processes with wide-sense stationary ergodic increments. Clustering synthetic data and real-world data are provided as examples of applications.
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Covariance-based Dissimilarity Measures Applied to Clustering Wide-sense Stationary Ergodic Processes
arXiv: Machine Learning, 2018Co-Authors: Qidi Peng, Nan Rao, Ran ZhaoAbstract:We introduce a new unsupervised learning problem: clustering wide-sense stationary ergodic stochastic processes. A covariance-based dissimilarity measure together with asymptotically consistent algorithms is designed for clustering offline and online datasets, respectively. We also suggest a Formal Criterion on the efficiency of dissimilarity measures, and discuss of some approach to improve the efficiency of our clustering algorithms, when they are applied to cluster particular type of processes, such as self-similar processes with wide-sense stationary ergodic increments. Clustering synthetic data and real-world data are provided as examples of applications.
Mauricio Gonzalez-soto - One of the best experts on this subject based on the ideXlab platform.
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von Neumann-Morgenstern and Savage Theorems for Causal Decision Making.
arXiv: Artificial Intelligence, 2019Co-Authors: Mauricio Gonzalez-soto, Luis Enrique Sucar, Hugo Jair EscalanteAbstract:Decision making under uncertain conditions has been well studied when uncertainty can only be considered at the associative level of information. The classical Theorems of von Neumann-Morgenstern and Savage provide a Formal Criterion for rationally making choices using associative information. We provide here a previous result from Pearl and show that it can be considered as a causal version of the von Neumann-Morgenstern Theorem; furthermore, we consider the case when the true causal mechanism that controls the environment is unknown to the decision maker and propose a causal version of the Savage Theorem. As applications, we argue how previous optimal action learning methods for causal environments fit within the Causal Savage Theorem we present thus showing the utility of our result in the justification and design of learning algorithms; furthermore, we define a Causal Nash Equilibria for a strategic game in a causal environment in terms of the preferences induced by our Causal Decision Making Theorem.