检索结果-内蒙古大学图书馆

A Practical probabilistic graphical Modeling Tool for Weighing Ecological Risk-Based Evidence

SOIL & SEDIMENT CONTAMINATION 2016年第4期25卷 476-487页

作者： Carriger, John F. Barron, Mace G. US EPA Natl Hlth & Environm Effects Res Lab Off Res & Dev ORISEGulf Ecol Div Gulf Breeze FL 32561 USA US EPA Natl Hlth & Environm Effects Res Lab Off Res & Dev Gulf Ecol Div 1 Sabine Isl Dr Gulf Breeze FL 32561 USA

Past weight-of-evidence frameworks for adverse ecological effects have provided soft-scoring procedures for judgments based on the quality and measured attributes of evidence. Here, we provide a flexible probabilistic structure for weighing and integrating lines of evidence for ecological risk determinations. probabilistic approaches can provide both a quantitative weighing of lines of evidence and methods for evaluating risk and uncertainty. The current modeling structure was developed for propagating uncertainties in measured endpoints and their influence on the plausibility of adverse effects. To illustrate the approach, we apply the model framework to the sediment quality triad using example lines of evidence for sediment chemistry measurements, bioassay results, and in situ infauna diversity of benthic communities using a simplified hypothetical case study. We then combine the three lines evidence and evaluate sensitivity to the input parameters, and show how uncertainties are propagated and how additional information can be incorporated to rapidly update the probability of impacts. The developed network model can be expanded to accommodate additional lines of evidence, variables and states of importance, and different types of uncertainties in the lines of evidence including spatial and temporal as well as measurement errors.

关键词： Bayesian belief networks ecological risk assessment probabilistic graphical models sediment quality criteria sediment quality triad weight-of-evidence

来源：评论

学校读者我要写书评

暂无评论

On Using Sum-Product Networks For Multi-Label Classification 6

On Using Sum-Product Networks For Multi-Label Classification

引用

6th Brazilian Conference on Intelligent Systems (BRACIS)

作者： Llerena, Julissa Villanueva Maua, Denis Deratani Univ Sao Paulo Inst Matemat & Estat Sao Paulo Brazil

ISBN: (纸本)9781538624074

Multi-Label classification consists of mapping each object to a set of relevant labels. A successful approach to constructing Multi-Label classifiers is to obtain a probabilistic model of the relation between object attributes and labels. This model can then be used to classify objects by computing the most probable configuration of label relevance variable conditioned on the attributes. Sum-Product Networks (SPN) are deep probabilistic models that have shown promising results in many tasks. As with many other probabilistic models, performing Most Probable Explanation (MPE) inference is NP-hard in SPNs. In this work we investigate the use of SPNs for Multi-Label classification. We compare different approaches for learning SPNs and computing MPE. We show that SPN-based Multi-Label Classifiers are competitive against state-of-the-art classifiers in a collection of real-world experiments.

关键词： Multi-label classification probabilistic graphical models sum-product networks

来源：评论

学校读者我要写书评

暂无评论

SPATIAL-TEMPORAL CONDITIONAL RANDOM FIELD BASED MODEL FOR CROP RECOGNITION IN TROPICAL REGIONS 37

SPATIAL-TEMPORAL CONDITIONAL RANDOM FIELD BASED MODEL FOR CR...

引用

IEEE International Geoscience & Remote Sensing Symposium

作者： Achanccaray, P. Feitosa, R. Q. Rottensteiner, F. Sanches, I. D. Heipke, C. Pontifical Catholic Univ Rio de Janeiro Dept Elect Engn Rio De Janeiro Brazil Univ Estado Rio De Janeiro Dept Comp Engn Rio De Janeiro Brazil Leibniz Univ Hannover Inst Photogrammetry & GeoInformat Hannover Germany Natl Inst Space Res Remote Sensing Div Sao Jose Dos Campos Brazil

ISBN: (纸本)9781509049516

This work presents a spatio-temporal Conditional Random Field (CRF) based model for crop recognition from multi-temporal remote sensing image sequences. The association potential at each image site is based on the class posterior probabilities computed by a Random Forest (RF) classifier given the features at the corresponding site. A contrast-sensitive Potts model is used as a label smoothing method in the spatial domain, whereas the interactions in the temporal domain are modeled based on expert knowledge about the possible transitions between adjacent epochs. The CRF based model was tested for crop mapping in two subtropical areas based on a sequences of 9 Landsat and 14 Sentinel-1 images from Ipua, Sao Paulo and Campo Verde, Mato Grosso, respectively, two municipalities in Brazil. The experiments showed significant improvements of the accumulated F1 score per class against a mono-temporal CRF approach of up to 50% and 75% for a total of 8 and 11 classes using Optical and SAR images respectively.

关键词： remote sensing probabilistic graphical models crop recognition Landsat images Sentinel-1

来源：评论

学校读者我要写书评

暂无评论

A Bayesian Active Learning Experimental Design for Inferring Signaling Networks 1

引用

21st Annual International Conference on Research in Computational Molecular Biology (RECOMB)

作者： Ness, Robert Osazuwa Sachs, Karen Mallick, Parag Vitek, Olga Purdue Univ Dept Stat W Lafayette IN 47907 USA Northeastern Univ Coll Sci Coll Comp & Informat Sci Boston MA 02115 USA Stanford Univ Sch Med Palo Alto CA 94305 USA

ISBN: (数字)9783319569703

ISBN: (纸本)9783319569703;9783319569697

Machine learning methods for learning network structure, applied to quantitative proteomics experiments, reverse-engineer intracellular signal transduction networks. They provide insight into the rewiring of signaling within the context of a disease or a phenotype. To learn the causal patterns of influence between proteins in the network, the methods require experiments that include targeted interventions that fix the activity of specific proteins. However, the interventions are costly and add experimental complexity. We describe a active learning strategy for selecting optimal interventions. Our approach takes as inputs pathway databases and historic datasets, expresses them in form of prior probability distributions on network structures, and selects interventions that maximize their expected contribution to structure learning. Evaluations on simulated and real data show that the strategy reduces the detection error of validated edges as compared to an unguided choice of interventions, and avoids redundant interventions, thereby increasing the effectiveness of the experiment.

关键词： Machine learning Active learning Causal inference Bayesian network probabilistic graphical models Biological networks

来源：评论

学校读者我要写书评

暂无评论

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

来源：评论

学校读者我要写书评

暂无评论

Advanced Algorithms for Medical Decision Analysis. Implementation in OpenMarkov 16th

Advanced Algorithms for Medical Decision Analysis. Implement...

引用

16th Conference on Artificial Intelligence in Medicine (AIME)

作者： Arias, Manuel Angel Artaso, Miguel Bermejo, Inigo Javier Diez, Francisco Luque, Manuel Perez-Martin, Jorge UNED Dept Artificial Intelligence Madrid Spain Univ Sheffield Sch Hlth & Related Res Sheffield S Yorkshire England

ISBN: (纸本)9783319597584;9783319597577

In spite the important advantages of influence diagrams over decision trees, including the possibility of solving much more complex problems, the medical literature still contains around 10 decision trees for each influence diagram. In this paper we analyse the reasons for the low acceptance of influence diagrams in health decision analysis, in contrast with its success in artificial intelligence. One of the reasons is the difficulty of representing asymmetric problems. Another one was the lack of algorithms for explaining the reasoning and performing cost-effectiveness analysis, as well as the scarcity of user-friendly software tools for sensitivity analysis. In this paper we review the research conducted by our group in the last 25 years, crystallised in the open-source software tool OpenMarkov, explaining how it has tried to address those challenges.

关键词： probabilistic graphical models Bayesian networks Influence diagrams Markov models Cost-effectiveness analysis Sensitivity analysis OpenMarkov

来源：评论

学校读者我要写书评

暂无评论

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

来源：评论

学校读者我要写书评

暂无评论

Matrix-Vector Algorithms of Global Posteriori Inference in Algebraic Bayesian Networks 20

Matrix-Vector Algorithms of Global Posteriori Inference in A...

引用

20th IEEE International Conference on Soft Computing and Measurements (SCM)

作者： Zolotin, Andrey A. Tulupyev, Alexander L. St Petersburg State Univ Comp Sci Dept St Petersburg Russia SPIIRAS Theoret & Interdisciplinary Comp Sci Lab St Petersburg Russia

ISBN: (纸本)9781538618103

Algorithm of global posteriori inference in algebraic Bayesian networks is considered in the paper. The results obtained earlier for local a posteriori inference are briefly presented. Main steps of global propagation algorithm are described in details. A transition matrix from the vector of knowledge pattern elements to the virtual evidence, propagated to the next knowledge pattern, is proposed. The stated theorem describes the matrix-vector representation of the stochastic evidence propagation algorithm within a network with scalar estimates of the knowledge patterns elements probabilities of truth. The obtained results form the basis for development of the global posteriori inference machine matrix-vector representation in algebraic Bayesian networks and simplify its further software implementation.

关键词： algebraic Bayesian networks probabilistic graphical models global inference matrix-vector equations ideal of conjuncts

来源：评论

学校读者我要写书评

暂无评论

AMUSE: Multilingual Semantic Parsing for Question Answering over Linked Data 16th

AMUSE: Multilingual Semantic Parsing for Question Answering ...

引用

16th International Semantic Web Conference (ISWC)

作者： Hakimov, Sherzod Jebbara, Soufian Cimiano, Philipp Bielefeld Univ Ctr Excellence CITEC Cognit Interact Technol Semant Comp Grp D-33615 Bielefeld Germany

ISBN: (纸本)9783319682884;9783319682877

The task of answering natural language questions over RDF data has received wide interest in recent years, in particular in the context of the series of QALD benchmarks. The task consists of mapping a natural language question to an executable form, e.g. SPARQL, so that answers from a given KB can be extracted. So far, most systems proposed are (i) monolingual and (ii) rely on a set of hard-coded rules to interpret questions and map them into a SPARQL query. We present the first multilingual QALD pipeline that induces a model from training data for mapping a natural language question into logical form as probabilistic inference. In particular, our approach learns to map universal syntactic dependency representations to a language-independent logical form based on DUDES (Dependency-based Underspecified Discourse Representation Structures) that are then mapped to a SPARQL query as a deterministic second step. Our model builds on factor graphs that rely on features extracted from the dependency graph and corresponding semantic representations. We rely on approximate inference techniques, Markov Chain Monte Carlo methods in particular, as well as Sample Rank to update parameters using a ranking objective. Our focus lies on developing methods that overcome the lexical gap and present a novel combination of machine translation and word embedding approaches for this purpose. As a proof of concept for our approach, we evaluate our approach on the QALD-6 datasets for English, German & Spanish.

关键词： Question answering Multilinguality QALD probabilistic graphical models Factor graphs

来源：评论

学校读者我要写书评

暂无评论

Computation Mechanism for Situated Sentient Robot

Computation Mechanism for Situated Sentient Robot

引用

Computing Conference

作者： Stipancic, Tomislav Ohmoto, Yoshimasa Badssi, Sara Akaoka Nishida, Toyoaki Kyoto Univ Grad Sch Informat Kyoto Japan

ISBN: (纸本)9781509054435

Emotions shape how we remember our world, how we perceive it, and which decisions we take. This study proposed a novel three-component computation mechanism that enables a robot to reason about emotions. The mechanism contains the following parts: (i) information acquisition, (ii) formal knowledge description in a form of ontology, and (iii) Bayesian Network (BN). An entropy reduction method from information theory is used to design an effective Human-Robot Interaction (HRI) and to gain a deeper understanding about proposed reasoning mechanism. The method also revealed the most influential BN variables that efficiently resolve the reasoning ambiguities. The modified OCC model of emotions in BN is implemented to ensure adaptation of the system to multiple sources of uncertainty. Variables in a hand crafted BN are linked together, where each link is quantified by spreading influences of a different strength that parent nodes have on child nodes. The paper introduces also the system architecture for realization of the physical robot setup.

关键词： affective robotics emotions context-awareness probabilistic graphical models ontology ubiquitous computing

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：