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检索条件"主题词=Probabilistic Graphical Models"

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Augmented Feature-StAte Sensors in Human Activity Recognition 9

Augmented Feature-StAte Sensors in Human Activity Recognitio...

引用

9th International Conference on Information and Knowledge Technology (IKT)

作者： Keyvanpour, Mohammad Reza Zolfaghari, Samaneh Alzahra Univ Dept Comp Engn Tehran Iran Alzahra Univ Data Min Lab Tehran Iran

ISBN: (纸本)9781538625477

Nowadays, Human Activity Recognition (HAR) has gain a lot of interest because of demand growth in many applications particularly in smart homes as a fundamental task. This problem is typically addressed as a supervised learning problem with the goal of learning the mapping of extracted related features out of sensors data to the underlying human activities. Most of the proposed methods for HAR do not consider important information such as time domain features explicitly for activity modeling. In this paper, Augmented Feature-StAte (Statistical-Activity context) Sensors (AFSSs) are proposed to incorporate combination of important statistical features and activity context information. To evaluate the proposed AFSSs, they are applied in four benchmark and popular probabilistic graphical activity recognition algorithms including Naive Bayesian classifiers (NBCs), Hidden Markov models (HMMs), Hidden Semi Markov models (HSMMs) and Linear-Chain Conditional Random Fields (LCCRFs). The experiments are performed on three well-known and real-world datasets in this field. The results show that the proposed AFSSs improve the classification performance particularly in terms of F1-Score, accuracy and robustness.

关键词： Activity recognition Statistical features Activity context features Augmented sensors probabilistic graphical models Smart homes

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Optimal selection of the most informative nodes in Opinion Dynamics on Networks 22

Optimal selection of the most informative nodes in Opinion D...

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22nd World Congress of the International Federation of Automatic Control (IFAC)

作者： Raineri, Roberta Como, Giacomo Fagnani, Fabio Politecn Torino Dept Math Sci GL Lagrange Corso Duca Abruzzi 24 I-10129 Turin Italy

ISBN: (纸本)9781713872344

Finding the optimal subset to observe in a network system is a fundamental problem in science and engineering, with a wide range of applications like monitoring spatial phenomena, control of epidemic spread, feature selection in machine learning, or active surveying in social studies. The goal of this paper is to address the subset selection problem on an Opinion Dynamics model where the variable of interest Y is the average opinion of the community. We consider the opinion vector X to be updated according to a Friedkin-Johnsen opinion dynamics model where every agent i is equipped with an original unknown belief ui, which is assumed to be normally distributed, and a parameter.i describing its openness to interactions. The objective function of the optimization problem is the variance reduction from the observation of the steady-state opinions of a subset K. V of agents. We show how this functional can be rewritten in terms of the Bonacich centrality and the cycle centrality of the agents in social network when the subset selection is of cardinality 1, providing particular graph-theoretic interpretations related to the network itself. In addition, first exploratory simulations highlight a behaviour which deviates from the one of known centrality measures depending on the choice of model parameters. Finally, we show that the submodularity of the functional is not guaranteed in our case and thus results taken from known literature are non-enforceable. This paves the way for further analysis.

关键词： Opinion Dynamics Centrality measures Network centrality Network systems Subset selection probabilistic graphical models Gaussian Random Fields

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Discovery and density estimation of latent confounders in Bayesian networks with evidence lower bound 11

Discovery and density estimation of latent confounders in Ba...

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International Conference on probabilistic graphical models

作者： Chobtham, Kiattikun Constantinou, Anthony C. Queen Mary Univ London Sch Elect Engn & Comp Sci Bayesian Artificial Intelligence Res Lab London E1 4NS England

Discovering and parameterising latent confounders represent important and challenging problems in causal structure learning and density estimation respectively. In this paper, we focus on both discovering and learning the distribution of latent confounders. This task requires solutions that come from different areas of statistics and machine learning. We combine elements of variational Bayesian methods, expectation-maximisation, hill-climbing search, and structure learning under the assumption of causal insufficiency. We propose two learning strategies;one that maximises model selection accuracy, and another that improves computational efficiency in exchange for minor reductions in accuracy. The former strategy is suitable for small networks and the latter for moderate size networks. Both learning strategies perform well relative to existing solutions.

关键词： Ancestral graphs EM algorithm greedy search hill-climbing search hidden variables probabilistic graphical models variational inference

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Highly Efficient Structural Learning of Sparse Staged Trees 11

Highly Efficient Structural Learning of Sparse Staged Trees

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International Conference on probabilistic graphical models

作者： Leonelli, Manuele Varando, Gherardo IE Univ Sch Sci & Technol Madrid Spain Univ Valencia Image Proc Lab Valencia Spain

Several structural learning algorithms for staged tree models, an asymmetric extension of Bayesian networks, have been defined. However, they do not scale efficiently as the number of variables considered increases. Here we introduce the first scalable structural learning algorithm for staged trees, which searches over a space of models where only a small number of dependencies can be imposed. A simulation study as well as a real-world application illustrate our routines and the practical use of such data-learned staged trees.

关键词： Asymmetric conditional independence Bayesian networks probabilistic graphical models Staged trees Structural learning

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Learning and Testing Latent-Tree Ising models Efficiently 36

Learning and Testing Latent-Tree Ising Models Efficiently

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36th Annual Conference on Learning Theory (COLT)

作者： Dagan, Yuval Daskalakis, Constantinos Kandiros, Anthimos-Vardis Choo, Davin MIT Dept Elect Engn & Comp Sci Cambridge MA 02139 USA Natl Univ Singapore Sch Comp Singapore Singapore

We provide time- and sample-efficient algorithms for learning and testing latent-tree Ising models, i.e. Ising models that may only be observed at their leaf nodes. On the learning side, we obtain efficient algorithms for learning a tree-structured Ising model whose leaf node distribution is close in total variation distance, improving on the results of Cryan et al. (2001). On the testing side, we provide an efficient algorithm with fewer samples for testing whether two latent-tree Ising models have leaf-node distributions that are close or far in total variation distance. We obtain our algorithms by showing novel localization results for the total variation distance between the leaf-node distributions of tree-structured Ising models, in terms of their marginals on pairs of leaves.

关键词： probabilistic graphical models distribution learning/testing learning from complex or structured data (e.g. networks, time series) learning with algebraic or combinatorial structure

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Discovering the Temporal Interactions between Clinical Observations 3

Discovering the Temporal Interactions between Clinical Obser...

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International Conference on Healthcare Informatics (ICHI)

作者： Goodwin, Travis Univ Texas Dallas Human Language Technol Res Inst Dallas TX 75080 USA

ISBN: (纸本)9781467395489

The explosion of clinical information provided by the advent of electronic health records (EHRs) offers an exciting opportunity to substantially improve the quality of health care. Updated throughout each patient's health care, EHRs document a wide variety of clinical observations, such as the patient's diagnoses, risk factors, medications, and test results at various points in the patient's history. This allows for the secondary use of EHRs to provide substantial information about the way that patients' clinical pictures and therapies have evolved over time. In this abstract, we outline our ongoing work towards harnessing the chronological, natural language text in EHRs in order to model how individual patients' clinical pictures and therapies have evolved. Moreover, we show our experiments on improving predictive accuracy by discovering latent groups of "similar" patients.

关键词： graphical models History Informatics Medical treatment Predictive models probabilistic logic disease progression probabilistic graphical models temporal prediction probabilistic logic graphical models Informatics Prediction models MEDICAL TREATMENT Disease Progression Exploration Medicine medical history Patient history Quality of Health Care

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A Structured Approach to Understanding Recovery and Relapse in AA 18

A Structured Approach to Understanding Recovery and Relapse ...

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27th World Wide Web (WWW) Conference

作者： Zhang, Yue Ramesh, Arti Golbeck, Jennifer Sridhar, Dhanya Getoor, Lise SUNY Binghamton Binghamton NY 13902 USA Univ Maryland College Pk MD 20742 USA Univ Calif Santa Cruz Santa Cruz CA 95064 USA

ISBN: (纸本)9781450356398

Alcoholism, also known as Alcohol Use Disorder (AUD), is a serious problem affecting millions of people worldwide. Recovery from AUD is known to be challenging and often leads to relapse at various points after enrolling in a rehabilitation program such as Alcoholics Anonymous (AA). In this work, we take a structured approach to understand recovery and relapse from AUD using social media data. To do so, we combine linguistic and psychological attributes of users with relational features that capture useful structure in the user interaction network. We evaluate our models on AA-attending users extracted from the Twitter social network and predict recovery at two different points-90 days and 1 year after the user joins AA, respectively. Our experiments reveal that our structured approach is helpful in predicting recovery in these users. We perform extensive quantitative analysis of different groups of features and dependencies among them. Our analysis sheds light on the role of each feature group and how they combine to predict recovery and relapse. Finally, we present a qualitative analysis of the different reasons behind users relapsing to AUD. Our models and analysis are helpful in making meaningful predictions in scenarios where only a subset of features are available and can potentially be helpful in identifying and preventing relapse early.

关键词： Social Media Analysis probabilistic graphical models Recovery from alcoholism Alcoholics Anonymous

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An AI-based system for pricing diverse products and services

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29th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence

作者： Shakya, S. Chin, C. M. Owusu, G. BT Innovate & Design Intelligent Syst Res Ctr Ipswich IP5 3RE Suffolk England

ISBN: (纸本)9781848829824

This paper describes an applied research work that looks at different ways to effectively manage resources Particularly, it describes how revenue management techniques can be used to balance demand against capacity, and describes a system that uses different OR and AI techniques to intelligently price diverse products and services This system can produce pricing policies for wide range of products and services regardless of the model of demand used The system incorporates a model specification layer, which provides flexibility in defining the demand model for different products It also incorporates an optimisation layer, which takes the specified model as an input and produces the pricing and production guidelines for the product The system can be either used as a stand alone system or can be incorporated as a generic modelling and optimisation component within a larger revenue management system (C) 2009 Elsevier B V All rights reserved

关键词： Estimation of distribution algorithms Genetic algorithm Evolutionary algorithm Evolutionary computation probabilistic graphical models Artificial intelligence

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AcMC²: Accelerated Markov Chain Monte Carlo for probabilistic models 24

AcMC<SUP>2</SUP>: Accelerated Markov Chain Monte Carlo for P...

引用

24th International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS)

作者： Banerjee, Subho S. Kalbarczyk, Zbigniew T. Iyer, Ravishankar K. Univ Illinois Champaign IL 61820 USA

ISBN: (纸本)9781450362405

probabilistic models (PMs) are ubiquitously used across a variety of machine learning applications. They have been shown to successfully integrate structural prior information about data and effectively quantify uncertainty to enable the development of more powerful, interpretable, and efficient learning algorithms. This paper presents AcMC2, a compiler that transforms PMs into optimized hardware accelerators (for use in FPGAs or ASICs) that utilize Markov chain Monte Carlo methods to infer and query a distribution of posterior samples from the model. The compiler analyzes statistical dependencies in the PM to drive several optimizations to maximally exploit the parallelism and data locality available in the problem. We demonstrate the use of AcMC2 to implement several learning and inference tasks on a Xilinx Virtex-7 FPGA. AcMC2-generated accelerators provide a 47 - 100x improvement in runtime performance over a 6-core IBM Power8 CPU and a 8 - 18x improvement over an NVIDIA K80 GPU. This corresponds to a 753 - 1600x improvement over the CPU and 248 - 463x over the GPU in performance-per-watt terms.

关键词： Accelerator Markov Chain Monte Carlo probabilistic graphical models probabilistic Programming

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Real-Time Event Detection with Water Sensor Networks Using a Spatio-Temporal Model 1

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International Workshop on Database Systems for Advanced Applications (DASFAA)

作者： Mao, Yingchi Chen, Xiaoli Xu, Zhuoming Hohai Univ Coll Comp & Informat Nanjing 210098 Jiangsu Peoples R China

ISBN: (数字)9783319320557

ISBN: (纸本)9783319320557;9783319320540

Event detection with the spatio-temporal correlation is one of the most popular applications of wireless sensor networks. This kind of task trends to be a difficult problem of big data analysis due to the massive data generated from large-scale sensor networks like water sensor networks, especially in the context of real-time analysis. To reduce the computational cost of abnormal event detection and improve the response time, sensor node selection is needed to cut down the amount of data for the spatio-temporal correlation analysis. In this paper, a connected dominated set (CDS) approach is introduced to select backbone nodes from the sensor network. Furthermore, a spatio-temporal model is proposed to achieve the spatio-temporal correlation analysis, where Markov chain is adopted to model the temporal dependency among the different sensor nodes, and Bayesian Network (BN) is used to model the spatial dependency. The proposed approach and model have been applied to the real-time detection of urgent events (e.g. water pollution incidents) with water sensor networks. Preliminary experimental results on simulated data indicate that our solution can achieve better performance in terms of response time and scalability, compared to the simple threshold algorithm and the BN-only algorithm.

关键词： Event detection with sensor networks Big data analysis Spatial-temporal model Connected dominating set probabilistic graphical models

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