Graphical models

Xem 1-20 trên 151 kết quả Graphical models
  • We investigate hierarchical graphical models (HGMs) for automatically detecting decisions in multi-party discussions. Several types of dialogue act (DA) are distinguished on the basis of their roles in formulating decisions. HGMs enable us to model dependencies between observed features of discussions, decision DAs, and subdialogues that result in a decision. For the task of detecting decision regions, an HGM classifier was found to outperform non-hierarchical graphical models and support vector machines, raising the F1-score to 0.80 from 0.55. ...

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  • We present a novel approach for building verb subcategorization lexicons using a simple graphical model. In contrast to previous methods, we show how the model can be trained without parsed input or a predefined subcategorization frame inventory. Our method outperforms the state-of-the-art on a verb clustering task, and is easily trained on arbitrary domains.

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  • Most previous work on trainable language generation has focused on two paradigms: (a) using a statistical model to rank a set of generated utterances, or (b) using statistics to inform the generation decision process. Both approaches rely on the existence of a handcrafted generator, which limits their scalability to new domains. This paper presents BAGEL, a statistical language generator which uses dynamic Bayesian networks to learn from semantically-aligned data produced by 42 untrained annotators. ...

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  • Software Engineering: Chapter 5 - System Modelling includes Existing and planned system models, System perspectives, UML diagram types, Use of graphical models, System boundaries, The context of the MHC-PMS, Movies for Rent, Process model of involuntary detention.

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  • Nowadays, huge amount of multimedia data are being constantly generated in various forms from various places around the world. With ever increasing complexity and variability of multimedia data, traditional rule-based approaches where humans have to discover the domain knowledge and encode it into a set of programming rules are too costly and incompetent for analyzing the contents, and gaining the intelligence of this glut of multimedia data. The challenges in data complexity and variability have led to revolutions in machine learning techniques.

    pdf0p hotmoingay 03-01-2013 27 5   Download

  • This paper describes an unsupervised dynamic graphical model for morphological segmentation and bilingual morpheme alignment for statistical machine translation. The model extends Hidden Semi-Markov chain models by using factored output nodes and special structures for its conditional probability distributions. It relies on morpho-syntactic and lexical source-side information (part-of-speech, morphological segmentation) while learning a morpheme segmentation over the target language. Our model outperforms a competitive word alignment system in alignment quality. ...

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  • Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Spatio-Temporal Graphical-Model-Based Multiple Facial Feature Tracking

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  • We predict entity type distributions in Web search queries via probabilistic inference in graphical models that capture how entitybearing queries are generated. We jointly model the interplay between latent user intents that govern queries and unobserved entity types, leveraging observed signals from query formulations and document clicks. We apply the models to resolve entity types in new queries and to assign prior type distributions over an existing knowledge base.

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  • Unsupervised word alignment is most often modeled as a Markov process that generates a sentence f conditioned on its translation e. A similar model generating e from f will make different alignment predictions. Statistical machine translation systems combine the predictions of two directional models, typically using heuristic combination procedures like grow-diag-final. This paper presents a graphical model that embeds two directional aligners into a single model.

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  • Sitting at the intersection between statistics and machine learning, Dynamic Bayesian Networks have been applied with much success in many domains, such as speech recognition, vision, and computational biology. While Natural Language Processing increasingly relies on statistical methods, we think they have yet to use Graphical Models to their full potential. In this paper, we report on experiments in learning edit distance costs using Dynamic Bayesian Networks and present results on a pronunciation classification task. ...

    pdf8p bunbo_1 17-04-2013 21 2   Download

  • This book is concerned with the computational processing of 3D faces, with applications in human computer interaction. It is a discriplinary research area overlapping with computer vision, computer graphics, machine learning and HCI. Within the last 10 years, fast increase in performance of memory, display and processor speed has allowed the expansion of Computer Graphics. It has now overcome Image Processing in its achievement. In the 3D face field, the CG-generated faces are almost indiscernible from real faces. Still it requires manual drawing for each image and artistic skills.

    pdf145p batoan 04-08-2009 224 79   Download

  • Computer graphics is now used in various fields; for industrial, educational, medical and entertainment purposes. The aim of computer graphics is to visualize real objects and imaginary or other abstract items. In order to visualize various things, many technologies are necessary and they are mainly divided into two types in computer graphics: modeling and rendering technologies. This book covers the most advanced technologies for both types.

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  • Tài liệu tham khảo bằng tiếng Anh về nghệ thiật hội họa - Figure Drawing Without A Model - Graphic Narrative

    pdf17p audi123 31-07-2010 76 25   Download

  • Solid modeling (or modelling) is a consistent set of principles for mathematical and computer modeling of three dimensional solids. Solid modeling is distinguished from related areas of Geometric modeling and Computer graphics by its emphasis on physical fidelity [1]. Together, the principles of geometric and solid modeling form the foundation of Computer-aided design and in general support the creation, exchange, visualization, animation, interrogation, and annotation of digital models of physical objects....

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  • AD software can be divided based upon the technology used: . 2-D drawing. Its applications include, · mechanical part drawing · printed-circuit board design and layout · facilities layout · cartography . Basic 3-D drawing (such as wire-frame modelling) . Sculptured surfaces (such as surface modelling)

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  • Semantics is the research area touching the diversified domains such as: Philosophy, Information Science, Linguistics, Formal Semantics, Philosophy of Language and its constructs, Query Processing, Semantic Web, Pragmatics, Computational Semantics, Programming Languages, and Semantic Memory etc. The current book is a nice blend of number of great ideas, theories, mathematical models, and practical systems in diversified domains.

    pdf296p bi_bi1 09-07-2012 42 9   Download

  • Mathematics in Action: Algebraic, Graphical, and Trigonometric Problem Solving, Fourth Edition, is intended to help college mathematics students gain mathematical literacy in the real world and simultaneously help them build a solid foundation for future study in mathematics and other disciplines. Our team of fourteen faculty, primarily from the State University of New York and the City University of New York systems, used the AMATYC Crossroads standards to develop this threebook series to serve a very large population of college students at the pre-precalculus level.

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  • Author Richard A. Hawley Reviewer Adrian Licuriceanu Stefano Provenzano Indexer Acquisition Editor Mary Nadar Graphics Content Commissioning Editor Meeta Rajani Technical Editor Worrell Lewis Cover Work Pooja Chiplunkar Valentina D’silva Production Coordinator Pooja Chiplunkar Monica Ajmera Project Coordinator Esha Thakker Proofreader Mario Cecere

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  • Over the last decade, a Bayesian network has become a popular representation for encoding uncertain expert knowledge in expert systems. A Bayesian network is a graphical model for probabilistic relationships among a set of variables. It is a graphical model that encodes probabilistic relationships among variables of interest. When used in conjunction with statistical techniques, the graphical model has several advantages for data modeling. So what do Bayesian networks and Bayesian methods have to offer? There are at least four benefits described in the following....

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  • This book is designed as a step-by-step tutorial that can be read through from beginning to end, with each chapter building on the last. Each section, however, can also be used as a reference for implementing various camera models, special effects, etc. The chapters are filled with illustrations, screenshots, and example code, and each chapter is based around the creation of one or more example projects.

    pdf292p trasua_123 03-01-2013 38 7   Download


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