We describe two constraint-based methods that can be used to improve the recall of a shallow discourse parser based on conditional random field chunking. These method uses a set of natural structural constraints as we...
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ISBN:
(纸本)9782951740877
We describe two constraint-based methods that can be used to improve the recall of a shallow discourse parser based on conditional random field chunking. These method uses a set of natural structural constraints as well as others that follow from the annotation guidelines of the Penn Discourse Treebank. We evaluated the resulting systems on the standard test set of the PDTB and achieved a rebalancing of precision and recall with improved F-measures across the board. This was especially notable when we used evaluation metrics taking partial matches into account;for these measures, we achieved F-measure improvements of several points.
Biomarker signature identification in "omics" data is a complex challenge that requires specialized feature selection algorithms. The objective of these algorithms is to select the smallest set(s) of molecul...
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Biomarker signature identification in "omics" data is a complex challenge that requires specialized feature selection algorithms. The objective of these algorithms is to select the smallest set(s) of molecular quantities that are able to predict a given outcome (target) with maximal predictive performance. This task is even more challenging when the outcome comprises of multiple classes;for example, one may be interested in identifying the genes whose expressions allow discrimination among different types of cancer (nominal outcome) or among different stages of the same cancer, e.g. Stage 1, 2, 3 and 4 of Lung Adenocarcinoma (ordinal outcome). In this work, we consider a particular type of successful feature selection methods, named constraint-based, local causal discovery algorithms. These algorithms depend on performing a series of conditional independence tests. We extend these algorithms for the analysis of problems with continuous predictors and multi-class outcomes, by developing and equipping them with an appropriate conditional independence test procedure for both nominal and ordinal multi-class targets. The test is based on multinomial logistic regression and employs the log-likelihood ratio test for model selection. We present a comparative, experimental evaluation on seven real-world, high-dimensional, gene-expression datasets. Within the scope of our analysis the results indicate that the new conditional independence test allows the identification of smaller and better performing signatures for multi-class outcome datasets, with respect to the current alternatives for performing the independence tests.
We present an efficient and scalable constraint-based algorithm, called Hybrid Parents and Children (HPC), to learn the parents and children of a target variable in a Bayesian network. Finding those variables is an im...
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ISBN:
(纸本)9783642159381
We present an efficient and scalable constraint-based algorithm, called Hybrid Parents and Children (HPC), to learn the parents and children of a target variable in a Bayesian network. Finding those variables is an important first step in many applications including Bayesian network structure learning, dimensionality reduction and feature selection. The algorithm combines ideas from incremental and divide-and-conquer methods in a principled and effective way, while still being sound in the sample limit. Extensive empirical experiments are provided on public synthetic and real-world data sets of various sample sizes. The most noteworthy feature of HPC is its ability to handle large neighborhoods contrary to current CB algorithm proposals. The number of calls to the statistical test, en hence the run-time, is empirically on the order O(n(1.09)), where n is the number of variables, on the five benchmarks that we considered, and O(n(1.21)) on a real drug design characterized by 138,351 features.
In this paper we present an interactive dynamic simulator for virtual avatars. It allows creation and manipulation of objects in a collaborative way by virtual avatars or between virtual avatars and users. The users i...
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ISBN:
(纸本)9783540690566
In this paper we present an interactive dynamic simulator for virtual avatars. It allows creation and manipulation of objects in a collaborative way by virtual avatars or between virtual avatars and users. The users interact with the simulation environment using a haptic probe which provides force feedback. This dynamic simulator uses fast dynamics computation and constraint-based methods with friction. It is part of a general framework that is being devised for studies of collaborative scenarios with haptic feedback.
Resource allocation problems (RAPs)are naturally represented as constraint networks (CNs), with constraints of inequality among activities that compete for the same resources at the same time [5]. A large variety of t...
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ISBN:
(纸本)3540424210
Resource allocation problems (RAPs)are naturally represented as constraint networks (CNs), with constraints of inequality among activities that compete for the same resources at the same time [5]. A large variety of timetabling problems can be formulated as CNs with inequality constraints (representing time conflicts among classes of the same teacher, for example). A new algorithm for solving networks of RAPs is described and its detailed behavior is presented on a small example. The proposed algorithm delays assignments of resources to selected activities and processes the network during the assignment procedure, to select delays and values. The proposed algorithm is an enhancement to standard intelligent backtracking algorithms [13], is complete and performs better than two former approaches to solving CNs of resource allocation [15], [3], [4]. Results of comparing the proposed resource assignment algorithm to other ordering heuristics, on randomly generated networks, are reported.
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