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检索条件"主题词=Probabilistic Graphical Models"

共 399 条记录，以下是231-240 订阅

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Gated Bayesian Networks

Gated Bayesian Networks

引用

12th Scandinavian Conference on Artificial Intelligence (SCAI)

作者： Bendtsen, Marcus Pena, Jose M. Linkoping Univ IDA ADIT S-58183 Linkoping Sweden

ISBN: (纸本)9781614993308;9781614993292

This paper introduces a new probabilistic graphical model called gated Bayesian network (GBN). This model evolved from the need to represent real world processes that include several distinct phases. In essence a GBN is a model that combines several Bayesian networks (BN) in such a manner that they may be active or inactive during queries to the model. We use objects called gates to combine BNs, and to activate and deactivate them when predefined logical statements are satisfied. These statements are based on combinations of posterior probabilities of the variables in the BNs. Although GBN is a new formalism there are features of GBNs that are similar to other formalisms and research, including influence diagrams, context-specific independence and structural adaptation.

关键词： probabilistic graphical models Bayesian networks influence diagrams decision support dynamic systems

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Modeling How Students Learn to Program 12

Modeling How Students Learn to Program

引用

43rd ACM Technical Symposium on Computer Science Education (SIGCSE 2012)

作者： Piech, Chris Sahami, Mehran Koller, Daphne Cooper, Stephen Blikstein, Paulo Stanford Univ Dept Comp Sci Stanford CA 94305 USA

ISBN: (纸本)9781450310987

Despite the potential wealth of educational indicators expressed in a student's approach to homework assignments, how students arrive at their final solution is largely overlooked in university courses. In this paper we present a methodology which uses machine learning techniques to autonomously create a graphical model of how students in an introductory programming course progress through a homework assignment. We subsequently show that this model is predictive of which students will struggle with material presented later in the class.

关键词： probabilistic graphical models Hidden Markov Model Program Dissimilarity Metric Intelligent Tutor Student Progress Model

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Cloud Telemetry Modeling via Residual Gauss-Markov Random Fields 24

Cloud Telemetry Modeling via Residual Gauss-Markov Random Fi...

引用

24th Conference on Innovation in Clouds, Internet and Networks (ICIN)

作者： Landolfi, Nicholas C. O'Neill, Daniel C. Lall, Sanjay Stanford Univ Dept Comp Sci Stanford CA 94305 USA Stanford Univ Dept Elect Engn Stanford CA 94305 USA

ISBN: (纸本)9781728177052

Can probabilistic graphical models characterize cloud telemetry? This paper promotes the affirmative view. Cloud systems are large, connected, and dynamic. Consequently, data-based techniques to model their telemetry are high-dimensional, spatial, and unsupervised. Undirected probabilistic graphical models seem natural, but remain unexplored. We discuss one way around the limitation that cloud measurements violate usual assumptions of normality, and give a tractable estimation procedure for a candidate data model. As a preliminary test, we fit the model and use it to detect and localize anomalies in a synthetic environment and for a small-scale software system.

关键词： cloud performance monitoring anomaly detection and localization probabilistic graphical models Gauss-Markov random fields Markov networks

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Assessing the Impact of Single and Pairwise Slot Constraints in a Factor Graph Model for Template-Based Information Extraction 23rd

Assessing the Impact of Single and Pairwise Slot Constraints...

引用

23rd International Conference on Applications of Natural Language to Information Systems (NLDB)

作者： ter Horst, Hendrik Hartung, Matthias Klinger, Roman Brazda, Nicole Mueller, Hans Werner Cimiano, Philipp Bielefeld Univ CITEC Bielefeld Germany Univ Stuttgart IMS Stuttgart Germany IIIIU Dusseldorf CNR Dusseldorf Germany IIIIU Dusseldorf Neurol Dusseldorf Germany

ISBN: (纸本)9783319919478;9783319919461

Template-based information extraction generalizes over standard token-level binary relation extraction in the sense that it attempts to fill a complex template comprising multiple slots on the basis of information given in a text. In the approach presented in this paper, templates and possible fillers are defined by a given ontology. The information extraction task consists in filling these slots within a template with previously recognized entities or literal values. We cast the task as a structure prediction problem and propose a joint probabilistic model based on factor graphs to account for the interdependence in slot assignments. Inference is implemented as a heuristic building on Markov chain Monte Carlo sampling. As our main contribution, we investigate the impact of soft constraints modeled as single slot factors which measure preferences of individual slots for ranges of fillers, as well as pairwise slot factors modeling the compatibility between fillers of two slots. Instead of relying on expert knowledge to acquire such soft constraints, in our approach they are directly captured in the model and learned from training data. We show that both types of factors are effective in improving information extraction on a real-world data set of full-text papers from the biomedical domain. Pairwise factors are shown to particularly improve the performance of our extraction model by up to +0.43 points in precision, leading to an F-1 score of 0.90 for individual templates.

关键词： Ontology-based information extraction Slot filling probabilistic graphical models Soft constraints Database population

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Bayesian network structure learning with causal effects in the presence of latent variables 10

Bayesian network structure learning with causal effects in t...

引用

10th International Conference on probabilistic graphical models (PGM)

作者： Chobtham, Kiattikun Constantinou, Anthony C. Queen Mary Univ London Bayesian Artificial Intelligence Res Lab Risk & Informat Management Res Grp Sch Elect Engn & Comp Sci London England

Latent variables may lead to spurious relationships that can be misinterpreted as causal relationships. In Bayesian Networks (BNs), this challenge is known as learning under causal insufficiency. Structure learning algorithms that assume causal insufficiency tend to reconstruct the ancestral graph of a BN, where bi-directed edges represent confounding and directed edges represent direct or ancestral relationships. This paper describes a hybrid structure learning algorithm, called CCHM, which combines the constraint-based part of cFCI with hill-climbing score-based learning. The score-based process incorporates Pearl's do-calculus to measure causal effects, which are used to orientate edges that would otherwise remain undirected, under the assumption the BN is a linear Structure Equation Model where data follow a multivariate Gaussian distribution. Experiments based on both randomised and well-known networks show that CCHM improves the state-of-the-art in terms of reconstructing the true ancestral graph.

关键词： ancestral graphs causal discovery causal insufficiency probabilistic graphical models

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Joint and Pipeline probabilistic models for Fine-Grained Sentiment Analysis: Extracting Aspects, Subjective Phrases and their Relations

Joint and Pipeline Probabilistic Models for Fine-Grained Sen...

引用

IEEE 13th International Conference on Data Mining (ICDM)

作者： Klinger, Roman Cimiano, Philipp Univ Bielefeld CIT EC Semant Comp Grp D-33615 Bielefeld Germany

ISBN: (纸本)9780769551098

Sentiment analysis and opinion mining are often addressed as a text classification or entity recognition problem, involving the detection or classification of aspects and subjective phrases. Many approaches do not model the relation between aspects and subjective phrases explicitly, implicitly assuming that a subjective phrase refers to a certain aspect if they co-occur together in the same sentence, thus potentially sacrificing accuracy. Instead, in the approach presented in this paper, we model the relation between aspects and subjective phrases explicitly, exploiting a flexible model based on imperatively defined factor graphs (IDF). The extraction of subjective phrases, aspects and the relation between them is modeled as a joint inference problem and compared to a pure pipeline architecture. Our goal is to analyse and quantify to what extent a joint model outperforms a pipeline model in terms of extraction of aspects, subjective phrases and the relation between them. Our results show that, while we have a substantial improvement on predicting targets using a joint inference model, the performance on subjective phrase detection and relation extraction actually decreases only slightly.

关键词： fine-grained sentiment analysis probabilistic graphical models factorie imperatively defined factor graphs information extraction machine learning

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Novelty Detection Using graphical models for Semantic Room Classification

Novelty Detection Using Graphical Models for Semantic Room C...

引用

15th Portuguese Conference on Artificial Intelligence (EPIA 2011)

作者： Pinto, Andre Susano Pronobis, Andrzej Reis, Luis Paulo Univ Porto Fac Engn Dep Informat Engn P-4200465 Oporto Portugal Royal Inst Technol KTH Ctr Autonomous Syst SE-10044 Stockholm Sweden Univ Porto LIACC Artificial Intelligence & Comp Sci Lab P-4200465 Oporto Portugal

ISBN: (纸本)9783642247682

This paper presents an approach to the problem of novelty detection in the context of semantic room categorization. The ability to assign semantic labels to areas in the environment is crucial for autonomous agents aiming to perform complex human-like tasks and human interaction. However, in order to be robust and naturally learn the semantics from the human user, the agent must be able to identify gaps in its own knowledge. To this end, we propose a method based on graphical models to identify novel input which does not match any of the previously learnt semantic descriptions. The method employs a novelty threshold defined in terms of conditional and unconditional probabilities. The novelty threshold is then optimized using an unconditional probability density model trained from unlabelled data.

关键词： Novelty detection semantic data probabilistic graphical models room classification indoor environments robotics multi-modal classification

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People on Drugs: Credibility of User Statements in Health Communities 14

People on Drugs: Credibility of User Statements in Health Co...

引用

20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD)

作者： Mukherjee, Subhabrata Weikum, Gerhard Danescu-Niculescu-Mizil, Cristian Max Planck Inst Informat Saarbrucken Germany Max Planck Inst Software Syst Saarbrucken Germany

ISBN: (纸本)9781450329569

Online health communities are a valuable source of information for patients and physicians. However, such user-generated resources are often plagued by inaccuracies and misinformation. In this work we propose a method for automatically establishing the credibility of user-generated medical statements and the trustworthiness of their authors by exploiting linguistic cues and distant supervision from expert sources. To this end we introduce a probabilistic graphical model that jointly learns user trustworthiness, statement credibility, and language objectivity. We apply this methodology to the task of extracting rare or unknown side-effects of medical drugs-this being one of the problems where large scale non-expert data has the potential to complement expert medical knowledge. We show that our method can reliably extract side-effects and filter out false statements, while identifying trustworthy users that are likely to contribute valuable medical information.

关键词： Credibility Trustworthiness Objectivity Veracity probabilistic graphical models

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Financial Data Analysis with PGMs using AMIDST 16

Financial Data Analysis with PGMs using AMIDST

引用

16th IEEE International Conference on Data Mining (ICDM)

作者： Cabanas, Rafael Martinez, Ana M. Masegosa, Andres R. Ramos-Lopez, Dario Salmeron, Antonio Nielsen, Thomas D. Langseth, Helge Madsen, Anders L. Aalborg Univ Aalborg Denmark Norwegian Univ Sci & Technol Trondheim Norway Univ Almeria Almeria Spain Hugin Expert AS Aalborg Denmark

ISBN: (纸本)9781509059102

The AMIDST Toolbox is an open source Java 8 library for scalable learning of probabilistic graphical models (PGMs) based on both batch and streaming data. An important application domain with streaming data characteristics is the banking sector, where we may want to monitor individual customers (based on their financial situation and behavior) as well as the general economic climate. Using a real financial data set from a Spanish bank, we have previously proposed and demonstrated a novel PGM framework for performing this type of data analysis with particular focus on concept drift. The framework is implemented in the AMIDST Toolbox, which was also used to conduct the reported analyses. In this paper, we provide an overview of the toolbox and illustrate with code examples how the toolbox can be used for setting up and performing analyses of this particular type.

关键词： Concept drift Data streams Financial data Java 8 Latent variable models Machine learning probabilistic graphical models Scalable learning Variational methods

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Solving Trajectory Optimization Problems by Influence Diagrams 14th

Solving Trajectory Optimization Problems by Influence Diagra...

引用

14th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU)

作者： Vomlel, Jiri Kratochvil, Vaclav Czech Acad Sci Inst Informat Theory & Automat Vodarenskou Vezi 4 Prague 18208 8 Czech Republic Univ Econ Fac Management Prague Jarosovska 1117-2 Jindrichuv Hradec 37701 Czech Republic

ISBN: (纸本)9783319615813;9783319615806

Influence diagrams are decision-theoretic extensions of Bayesian networks. In this paper we show how influence diagrams can be used to solve trajectory optimization problems. These problems are traditionally solved by methods of optimal control theory but influence diagrams offer an alternative that brings benefits over the traditional approaches. We describe how a trajectory optimization problem can be represented as an influence diagram. We illustrate our approach on twowell-known trajectory optimization problems - the Brachistochrone Problem and the Goddard Problem. We present results of numerical experiments on these two problems, compare influence diagrams with optimal control methods, and discuss the benefits of influence diagrams.

关键词： Influence diagrams probabilistic graphical models Optimal control theory Brachistochrone problem Goddard problem

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