The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Qiang Yang - One of the best experts on this subject based on the ideXlab platform.
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deriving a stationary dynamic Bayesian Network from a logic program with recursive loops
Inductive Logic Programming, 2005Co-Authors: Yidong Shen, Qiang YangAbstract:Recursive loops in a logic program present a challenging problem to the PLP framework. On the one hand, they loop forever so that the PLP backward-chaining inferences would never stop. On the other hand, they generate cyclic influences, which are disallowed in Bayesian Networks. Therefore, in existing PLP approaches logic programs with recursive loops are considered to be problematic and thus are excluded. In this paper, we propose an approach that makes use of recursive loops to build a stationary dynamic Bayesian Network. Our work stems from an observation that recursive loops in a logic program imply a time sequence and thus can be used to model a stationary dynamic Bayesian Network without using explicit time parameters. We introduce a Bayesian knowledge base with logic clauses of the form A ← A1,...,Al, true, Context, Types, which naturally represents the knowledge that the Ais have direct influences on A in the context Context under the type constraints Types. We then use the well-founded model of a logic program to define the direct influence relation and apply SLG-resolution to compute the space of random variables together with their parental connections. We introduce a novel notion of influence clauses, based on which a declarative semantics for a Bayesian knowledge base is established and algorithms for building a two-slice dynamic Bayesian Network from a logic program are developed.
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deriving a stationary dynamic Bayesian Network from a logic program with recursive loops
arXiv: Artificial Intelligence, 2005Co-Authors: Yidong Shen, Qiang Yang, Liyan YuanAbstract:Recursive loops in a logic program present a challenging problem to the PLP framework. On the one hand, they loop forever so that the PLP backward-chaining inferences would never stop. On the other hand, they generate cyclic influences, which are disallowed in Bayesian Networks. Therefore, in existing PLP approaches logic programs with recursive loops are considered to be problematic and thus are excluded. In this paper, we propose an approach that makes use of recursive loops to build a stationary dynamic Bayesian Network. Our work stems from an observation that recursive loops in a logic program imply a time sequence and thus can be used to model a stationary dynamic Bayesian Network without using explicit time parameters. We introduce a Bayesian knowledge base with logic clauses of the form $A \leftarrow A_1,...,A_l, true, Context, Types$, which naturally represents the knowledge that the $A_i$s have direct influences on $A$ in the context $Context$ under the type constraints $Types$. We then use the well-founded model of a logic program to define the direct influence relation and apply SLG-resolution to compute the space of random variables together with their parental connections. We introduce a novel notion of influence clauses, based on which a declarative semantics for a Bayesian knowledge base is established and algorithms for building a two-slice dynamic Bayesian Network from a logic program are developed.
Yidong Shen - One of the best experts on this subject based on the ideXlab platform.
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deriving a stationary dynamic Bayesian Network from a logic program with recursive loops
Inductive Logic Programming, 2005Co-Authors: Yidong Shen, Qiang YangAbstract:Recursive loops in a logic program present a challenging problem to the PLP framework. On the one hand, they loop forever so that the PLP backward-chaining inferences would never stop. On the other hand, they generate cyclic influences, which are disallowed in Bayesian Networks. Therefore, in existing PLP approaches logic programs with recursive loops are considered to be problematic and thus are excluded. In this paper, we propose an approach that makes use of recursive loops to build a stationary dynamic Bayesian Network. Our work stems from an observation that recursive loops in a logic program imply a time sequence and thus can be used to model a stationary dynamic Bayesian Network without using explicit time parameters. We introduce a Bayesian knowledge base with logic clauses of the form A ← A1,...,Al, true, Context, Types, which naturally represents the knowledge that the Ais have direct influences on A in the context Context under the type constraints Types. We then use the well-founded model of a logic program to define the direct influence relation and apply SLG-resolution to compute the space of random variables together with their parental connections. We introduce a novel notion of influence clauses, based on which a declarative semantics for a Bayesian knowledge base is established and algorithms for building a two-slice dynamic Bayesian Network from a logic program are developed.
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deriving a stationary dynamic Bayesian Network from a logic program with recursive loops
arXiv: Artificial Intelligence, 2005Co-Authors: Yidong Shen, Qiang Yang, Liyan YuanAbstract:Recursive loops in a logic program present a challenging problem to the PLP framework. On the one hand, they loop forever so that the PLP backward-chaining inferences would never stop. On the other hand, they generate cyclic influences, which are disallowed in Bayesian Networks. Therefore, in existing PLP approaches logic programs with recursive loops are considered to be problematic and thus are excluded. In this paper, we propose an approach that makes use of recursive loops to build a stationary dynamic Bayesian Network. Our work stems from an observation that recursive loops in a logic program imply a time sequence and thus can be used to model a stationary dynamic Bayesian Network without using explicit time parameters. We introduce a Bayesian knowledge base with logic clauses of the form $A \leftarrow A_1,...,A_l, true, Context, Types$, which naturally represents the knowledge that the $A_i$s have direct influences on $A$ in the context $Context$ under the type constraints $Types$. We then use the well-founded model of a logic program to define the direct influence relation and apply SLG-resolution to compute the space of random variables together with their parental connections. We introduce a novel notion of influence clauses, based on which a declarative semantics for a Bayesian knowledge base is established and algorithms for building a two-slice dynamic Bayesian Network from a logic program are developed.
Liyan Yuan - One of the best experts on this subject based on the ideXlab platform.
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deriving a stationary dynamic Bayesian Network from a logic program with recursive loops
arXiv: Artificial Intelligence, 2005Co-Authors: Yidong Shen, Qiang Yang, Liyan YuanAbstract:Recursive loops in a logic program present a challenging problem to the PLP framework. On the one hand, they loop forever so that the PLP backward-chaining inferences would never stop. On the other hand, they generate cyclic influences, which are disallowed in Bayesian Networks. Therefore, in existing PLP approaches logic programs with recursive loops are considered to be problematic and thus are excluded. In this paper, we propose an approach that makes use of recursive loops to build a stationary dynamic Bayesian Network. Our work stems from an observation that recursive loops in a logic program imply a time sequence and thus can be used to model a stationary dynamic Bayesian Network without using explicit time parameters. We introduce a Bayesian knowledge base with logic clauses of the form $A \leftarrow A_1,...,A_l, true, Context, Types$, which naturally represents the knowledge that the $A_i$s have direct influences on $A$ in the context $Context$ under the type constraints $Types$. We then use the well-founded model of a logic program to define the direct influence relation and apply SLG-resolution to compute the space of random variables together with their parental connections. We introduce a novel notion of influence clauses, based on which a declarative semantics for a Bayesian knowledge base is established and algorithms for building a two-slice dynamic Bayesian Network from a logic program are developed.
R Sheltonchristian - One of the best experts on this subject based on the ideXlab platform.
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continuous time Bayesian Network reasoning and learning engine
Journal of Machine Learning Research, 2010Co-Authors: R SheltonchristianAbstract:We present a continuous time Bayesian Network reasoning and learning engine (CTBN-RLE). A continuous time Bayesian Network (CTBN) provides a compact (factored) description of a continuous-time Mark...
Shohreh Kasaei - One of the best experts on this subject based on the ideXlab platform.
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event detection and summarization in soccer videos using Bayesian Network and copula
IEEE Transactions on Circuits and Systems for Video Technology, 2014Co-Authors: Mostafa Tavassolipour, Mahmood Karimian, Shohreh KasaeiAbstract:Semantic video analysis and automatic concept extraction play an important role in several applications; including content-based search engines, video indexing, and video summarization. As the Bayesian Network is a powerful tool for learning complex patterns, a novel Bayesian Network-based method is proposed for automatic event detection and summarization in soccer videos. The proposed method includes efficient algorithms for shot boundary detection, shot view classification, mid-level visual feature extraction, and construction of the related Bayesian Network. The method contains of three main stages. In the first stage, the shot boundaries are detected. Using the hidden Markov model, the video is segmented into large and meaningful semantic units, called play-break sequences. In the next stage, several features are extracted from each of these units. Finally, in the last stage, in order to achieve high level semantic features (events and concepts), the Bayesian Network is used. The basic part of the method is constructing the Bayesian Network, for which the structure is estimated using the Chow-Liu tree. The joint distributions of random variables of the Network are modeled by applying the Farlie-Gumbel-Morgenstern family of Copulas. The performance of the proposed method is evaluated on a dataset with about 9 h of soccer videos. The method is capable of detecting seven different events in soccer videos; namely, goal, card, goal attempt, corner, foul, offside, and nonhighlights. Experimental results show the effectiveness and robustness of the proposed method on detecting these events.
