The Experts below are selected from a list of 13137 Experts worldwide ranked by ideXlab platform
Pascal Schweitzer - One of the best experts on this subject based on the ideXlab platform.
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Walk refinement, walk logic, and the Iteration Number of the Weisfeiler-Leman algorithm
2019 34th Annual ACM IEEE Symposium on Logic in Computer Science (LICS), 2019Co-Authors: Moritz Lichter, Ilia Ponomarenko, Pascal SchweitzerAbstract:We show that the 2-dimensional Weisfeiler-Leman algorithm stabilizes n-vertex graphs after at most O(n log n) Iterations. This implies that if such graphs are distinguishable in 3-variable first order logic with counting, then they can also be distinguished in this logic by a formula of quantifier depth at most O(n log n). For this we exploit a new refinement based on counting walks and argue that its Iteration Number differs from the classic Weisfeiler-Leman refinement by at most a logarithmic factor. We then prove matching linear upper and lower bounds on the Number of Iterations of the walk refinement. This is achieved with an algebraic approach by exploiting properties of semisimple matrix algebras. We also define a walk logic and a bijective walk pebble game that precisely correspond to the new walk refinement.
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LICS - Walk refinement, walk logic, and the Iteration Number of the Weisfeiler-Leman algorithm
2019 34th Annual ACM IEEE Symposium on Logic in Computer Science (LICS), 2019Co-Authors: Moritz Lichter, Ilia Ponomarenko, Pascal SchweitzerAbstract:We show that the 2-dimensional Weisfeiler-Leman algorithm stabilizes n-vertex graphs after at most $\mathcal{O}(n\log n)$ Iterations. This implies that if such graphs are distinguishable in 3-variable first order logic with counting, then they can also be distinguished in this logic by a formula of quantifier depth at most $\mathcal{O}(n\log n)$ . For this we exploit a new refinement based on counting walks and argue that its Iteration Number differs from the classic Weisfeiler-Leman refinement by at most a logarithmic factor. We then prove matching linear upper and lower bounds on the Number of Iterations of the walk refinement. This is achieved with an algebraic approach by exploiting properties of semisimple matrix algebras. We also define a walk logic and a bijective walk pebble game that precisely correspond to the new walk refinement.
Hua Zhang - One of the best experts on this subject based on the ideXlab platform.
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study on subtractive clustering video moving object locating method with introduction of eigengap
Fuzzy Systems and Knowledge Discovery, 2012Co-Authors: Wenhui Zhou, Ertao Li, Hua ZhangAbstract:Considering the problem that the Iteration Number of video moving object locating method based on subtractive clustering existing a lot of uncertainties, a new video moving object locating method based on subtractive clustering introducing eigengap was proposed. Considering the data density function of subtractive clustering method, affinity matrix and the diagonal matrix was constructed, then eigenvalue and eigengap were both computed using normalized affinity matrix, the Iteration Number of subtractive clustering video moving object locating method could be automatically determined using the first maximum eigengap value. The extraordinary feature of proposed method was that the affinity matrix and the diagonal matrix could be both determined while computing density value, and then the Iteration Number could be automatically determined using eigengap. Experiment results showed that the proposed method had a better performance on dealing with the Iteration Number of subtractive clustering locating method.
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FSKD - Study on subtractive clustering video moving object locating method with introduction of eigengap
2012 9th International Conference on Fuzzy Systems and Knowledge Discovery, 2012Co-Authors: Wenhui Zhou, Ertao Li, Hua ZhangAbstract:Considering the problem that the Iteration Number of video moving object locating method based on subtractive clustering existing a lot of uncertainties, a new video moving object locating method based on subtractive clustering introducing eigengap was proposed. Considering the data density function of subtractive clustering method, affinity matrix and the diagonal matrix was constructed, then eigenvalue and eigengap were both computed using normalized affinity matrix, the Iteration Number of subtractive clustering video moving object locating method could be automatically determined using the first maximum eigengap value. The extraordinary feature of proposed method was that the affinity matrix and the diagonal matrix could be both determined while computing density value, and then the Iteration Number could be automatically determined using eigengap. Experiment results showed that the proposed method had a better performance on dealing with the Iteration Number of subtractive clustering locating method.
Minoru Sasaki - One of the best experts on this subject based on the ideXlab platform.
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unsupervised learning of word sense disambiguation rules by estimating an optimum Iteration Number in the em algorithm
North American Chapter of the Association for Computational Linguistics, 2003Co-Authors: Hiroyuki Shinnou, Minoru SasakiAbstract:In this paper, we improve an unsupervised learning method using the Expectation-Maximization (EM) algorithm proposed by Nigam et al. for text classification problems in order to apply it to word sense disambiguation (WSD) problems. The improved method stops the EM algorithm at the optimum Iteration Number. To estimate that Number, we propose two methods. In experiments, we solved 50 noun WSD problems in the Japanese Dictionary Task in SENSEVAL2. The score of our method is a match for the best public score of this task. Furthermore, our methods were confirmed to be effective also for verb WSD problems.
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CoNLL - Unsupervised learning of word sense disambiguation rules by estimating an optimum Iteration Number in the EM algorithm
Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 -, 2003Co-Authors: Hiroyuki Shinnou, Minoru SasakiAbstract:In this paper, we improve an unsupervised learning method using the Expectation-Maximization (EM) algorithm proposed by Nigam et al. for text classification problems in order to apply it to word sense disambiguation (WSD) problems. The improved method stops the EM algorithm at the optimum Iteration Number. To estimate that Number, we propose two methods. In experiments, we solved 50 noun WSD problems in the Japanese Dictionary Task in SENSEVAL2. The score of our method is a match for the best public score of this task. Furthermore, our methods were confirmed to be effective also for verb WSD problems.
Kenneth E. Barner - One of the best experts on this subject based on the ideXlab platform.
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EUSIPCO - In-network cooperative spectrum sensing
2009Co-Authors: Mehmet E. Yildiz, Tuncer C. Aysal, Kenneth E. BarnerAbstract:This paper proposes a distributed average consensus algorithm in order to solve the cooperative spectrum sensing task without a cognitive base station. The proposed consensus algorithm converges to the optimal decision statistic in the limit. Since in practice the Iteration Number has to be finite, we derive high probability bounds on the Iteration Number at which all the CRs are at most e away from the optimal decision statistic. Moreover, we compute the performance characteristics of the proposed in-network cooperative spectrum sensing at a given Iteration.
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In-network cooperative spectrum sensing
2009 17th European Signal Processing Conference, 2009Co-Authors: Mehmet E. Yildiz, Tuncer C. Aysal, Kenneth E. BarnerAbstract:This paper proposes a distributed average consensus algorithm in order to solve the cooperative spectrum sensing task without a cognitive base station. The proposed consensus algorithm converges to the optimal decision statistic in the limit. Since in practice the Iteration Number has to be finite, we derive high probability bounds on the Iteration Number at which all the CRs are at most ε away from the optimal decision statistic. Moreover, we compute the performance characteristics of the proposed in-network cooperative spectrum sensing at a given Iteration.
Moritz Lichter - One of the best experts on this subject based on the ideXlab platform.
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Walk refinement, walk logic, and the Iteration Number of the Weisfeiler-Leman algorithm
2019 34th Annual ACM IEEE Symposium on Logic in Computer Science (LICS), 2019Co-Authors: Moritz Lichter, Ilia Ponomarenko, Pascal SchweitzerAbstract:We show that the 2-dimensional Weisfeiler-Leman algorithm stabilizes n-vertex graphs after at most O(n log n) Iterations. This implies that if such graphs are distinguishable in 3-variable first order logic with counting, then they can also be distinguished in this logic by a formula of quantifier depth at most O(n log n). For this we exploit a new refinement based on counting walks and argue that its Iteration Number differs from the classic Weisfeiler-Leman refinement by at most a logarithmic factor. We then prove matching linear upper and lower bounds on the Number of Iterations of the walk refinement. This is achieved with an algebraic approach by exploiting properties of semisimple matrix algebras. We also define a walk logic and a bijective walk pebble game that precisely correspond to the new walk refinement.
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LICS - Walk refinement, walk logic, and the Iteration Number of the Weisfeiler-Leman algorithm
2019 34th Annual ACM IEEE Symposium on Logic in Computer Science (LICS), 2019Co-Authors: Moritz Lichter, Ilia Ponomarenko, Pascal SchweitzerAbstract:We show that the 2-dimensional Weisfeiler-Leman algorithm stabilizes n-vertex graphs after at most $\mathcal{O}(n\log n)$ Iterations. This implies that if such graphs are distinguishable in 3-variable first order logic with counting, then they can also be distinguished in this logic by a formula of quantifier depth at most $\mathcal{O}(n\log n)$ . For this we exploit a new refinement based on counting walks and argue that its Iteration Number differs from the classic Weisfeiler-Leman refinement by at most a logarithmic factor. We then prove matching linear upper and lower bounds on the Number of Iterations of the walk refinement. This is achieved with an algebraic approach by exploiting properties of semisimple matrix algebras. We also define a walk logic and a bijective walk pebble game that precisely correspond to the new walk refinement.
