The Experts below are selected from a list of 261 Experts worldwide ranked by ideXlab platform
Guergana K Savova - One of the best experts on this subject based on the ideXlab platform.
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Improving Temporal Relation Extraction with Training Instance Augmentation
BioNLP, 2016Co-Authors: Chen Lin, Timothy Miller, Steven Bethard, Dmitriy Dligach, Guergana K SavovaAbstract:Temporal relation extraction is important for understanding the ordering of events in narrative text. We describe a method for increasing the number of high-quality Training Instances available to a temporal relation extraction task, with an adaptation to different annotation styles in the clinical domain by taking advantage of the Unified Medical Language System (UMLS). This method notably improves clinical tempo-ral relation extraction, works beyond fea-turizing or duplicating the same informa-tion, can generalize between-argument sig-nals in a more effective and robust fashion. We also report a new state-of-the-art result, which is a two point improvement over the best Clinical TempEval 2016 system.
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BioNLP@ACL - Improving Temporal Relation Extraction with Training Instance Augmentation
Proceedings of the 15th Workshop on Biomedical Natural Language Processing, 2016Co-Authors: Chen Lin, Dmitriy Dligach, Steven Bethard, Timothy A. Miller, Guergana K SavovaAbstract:Temporal relation extraction is important for understanding the ordering of events in narrative text. We describe a method for increasing the number of high-quality Training Instances available to a temporal relation extraction task, with an adaptation to different annotation styles in the clinical domain by taking advantage of the Unified Medical Language System (UMLS). This method notably improves clinical temporal relation extraction, works beyond featurizing or duplicating the same information, can generalize between-argument signals in a more effective and robust fashion. We also report a new state-of-the-art result, which is a two point improvement over the best Clinical TempEval 2016 system.
Costas J Spanos - One of the best experts on this subject based on the ideXlab platform.
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towards efficient data valuation based on the shapley value
International Conference on Artificial Intelligence and Statistics, 2019Co-Authors: Ruoxi Jia, David Dao, Boxin Wang, Frances Ann Hubis, Nicholas Hynes, Nezihe Merve Gurel, Ce Zhang, Dawn Song, Costas J SpanosAbstract:{\em ``How much is my data worth?''} is an increasingly common question posed by organizations and individuals alike. An answer to this question could allow, for Instance, fairly distributing profits among multiple data contributors and determining prospective compensation when data breaches happen. In this paper, we study the problem of \emph{data valuation} by utilizing the Shapley value, a popular notion of value which originated in coopoerative game theory. The Shapley value defines a unique payoff scheme that satisfies many desiderata for the notion of data value. However, the Shapley value often requires \emph{exponential} time to compute. To meet this challenge, we propose a repertoire of efficient algorithms for approximating the Shapley value. We also demonstrate the value of each Training Instance for various benchmark datasets.
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towards efficient data valuation based on the shapley value
arXiv: Learning, 2019Co-Authors: Ruoxi Jia, David Dao, Boxin Wang, Frances Ann Hubis, Nicholas Hynes, Nezihe Merve Gurel, Ce Zhang, Dawn Song, Costas J SpanosAbstract:"How much is my data worth?" is an increasingly common question posed by organizations and individuals alike. An answer to this question could allow, for Instance, fairly distributing profits among multiple data contributors and determining prospective compensation when data breaches happen. In this paper, we study the problem of data valuation by utilizing the Shapley value, a popular notion of value which originated in coopoerative game theory. The Shapley value defines a unique payoff scheme that satisfies many desiderata for the notion of data value. However, the Shapley value often requires exponential time to compute. To meet this challenge, we propose a repertoire of efficient algorithms for approximating the Shapley value. We also demonstrate the value of each Training Instance for various benchmark datasets.
Haitao Zou - One of the best experts on this subject based on the ideXlab platform.
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Fuzzy Support Vector Machine With Relative Density Information for Classifying Imbalanced Data
IEEE Transactions on Fuzzy Systems, 2019Co-Authors: Changyin Sun, Xibei Yang, Shang Zheng, Haitao ZouAbstract:Fuzzy support vector machine (FSVM) has been combined with class imbalance learning (CIL) strategies to address the problem of classifying skewed data. However, the existing approaches hold several inherent drawbacks, causing the inaccurate prior data distribution estimation, further decreasing the quality of the classification model. To solve this problem, we present a more robust prior data distribution information extraction method named relative density, and two novel FSVM-CIL algorithms based on the relative density information in this paper. In our proposed algorithms, a K-nearest neighbors-based probability density estimation (KNN-PDE) alike strategy is utilized to calculate the relative density of each Training Instance. In particular, the relative density is irrelevant with the dimensionality of data distribution in feature space, but only reflects the significance of each Instance within its class; hence, it is more robust than the absolute distance information. In addition, the relative density can better seize the prior data distribution information, no matter the data distribution is easy or complex. Even for the data with small injunctions or a large class overlap, the relative density information can reflect its details well. We evaluated the proposed algorithms on an amount of synthetic and real-world imbalanced datasets. The results show that our proposed algorithms obviously outperform to some previous work, especially on those datasets with sophisticated distributions.
Jim Torresen - One of the best experts on this subject based on the ideXlab platform.
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an ant learning algorithm for gesture recognition with one Instance Training
Congress on Evolutionary Computation, 2013Co-Authors: Sichao Song, Arjun Chandra, Jim TorresenAbstract:In this paper, we introduce a novel gesture recognition algorithm named the ant learning algorithm (ALA), which aims at eliminating some of the limitations with the current leading algorithms, especially Hidden Markov Models. It requires minimal Training Instances and greatly reduces the computational overhead required by both Training and classification. ALA takes advantage of the pheromone mechanism from ant colony optimization. It uses pheromone tables to represent gestures, which scales well with gesture complexity. Our experimental results show that ALA can achieve a high recognition accuracy of 91.3% with only one Training Instance, and exhibits good generalization.
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IEEE Congress on Evolutionary Computation - An ant learning algorithm for gesture recognition with one-Instance Training
2013 IEEE Congress on Evolutionary Computation, 2013Co-Authors: Sichao Song, Arjun Chandra, Jim TorresenAbstract:In this paper, we introduce a novel gesture recognition algorithm named the ant learning algorithm (ALA), which aims at eliminating some of the limitations with the current leading algorithms, especially Hidden Markov Models. It requires minimal Training Instances and greatly reduces the computational overhead required by both Training and classification. ALA takes advantage of the pheromone mechanism from ant colony optimization. It uses pheromone tables to represent gestures, which scales well with gesture complexity. Our experimental results show that ALA can achieve a high recognition accuracy of 91.3% with only one Training Instance, and exhibits good generalization.
Frank Pfenning - One of the best experts on this subject based on the ideXlab platform.
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Higher-order and modal logic as a framework for explanation-based generalization
Machine Learning, 1992Co-Authors: Scott Dietzen, Frank PfenningAbstract:Certain tasks, such as formal program development and theorem proving, fundamentally rely upon the manipulation of higher-order objects such as functions and predicates. Computing tools intended to assist in performing these tasks are at present inadequate in both the amount of ‘knowledge’ they contain ( i.e. , the level of support they provide) and in their ability to ‘learn’ ( i.e. , their capacity to enhance that support over time). The application of a relevant machine learning technique—explanation-based generalization (EBG)—has thus far been limited to first-order problem representations. We extend EBG to generalize higher-order values, thereby enabling its application to higher-order problem encodings. Logic programming provides a uniform framework in which all aspects of explanation-based generalization and learning may be defined and carried out. First-order Horn logics ( e.g. , Prolog) are not, however, well suited to higher-order applications. Instead, we employ λProlog, a higher-order logic programming language, as our basic framework for realizing higher-order EBG. In order to capture the distinction between domain theory and Training Instance upon which EBG relies, we extend λProlog with the necessity operator □ of modal logic. We develop a meta-interpreter realizing EBG for the extended language, λ^□Prolog, and provide examples of higher-order EBG.
