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Probabilistic Graphical Models - Principles and Techniques
Most tasks require a person or an automated system to reason -- to reach conclusions based on available information. The framework of probabilistic graphical models, presented in this book, providesExpand
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FastSLAM: a factored solution to the simultaneous localization and mapping problem
The ability to simultaneously localize a robot and accurately map its surroundings is considered by many to be a key prerequisite of truly autonomous robots. However, few approaches to this problemExpand
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Support Vector Machine Active Learning with Applications to Text Classification
Support vector machines have met with significant success in numerous real-world learning tasks. However, like most machine learning algorithms, they are generally applied using a randomly selectedExpand
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Max-Margin Markov Networks
In typical classification tasks, we seek a function which assigns a label to a single object. Kernel-based approaches, such as support vector machines (SVMs), which maximize the margin of confidenceExpand
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Sequential Monte Carlo Methods in Practice
A package for fragile articles is provided which includes a container having a bottom surface delimited by upright walls, and a plurality of article-loaded trays arranged in a compact, stacked,Expand
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SCAPE: shape completion and animation of people
We introduce the SCAPE method (Shape Completion and Animation for PEople)---a data-driven method for building a human shape model that spans variation in both subject shape and pose. The method isExpand
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Self-Paced Learning for Latent Variable Models
Latent variable models are a powerful tool for addressing several tasks in machine learning. However, the algorithms for learning the parameters of latent variable models are prone to getting stuckExpand
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Decomposing a scene into geometric and semantically consistent regions
High-level, or holistic, scene understanding involves reasoning about objects, regions, and the 3D relationships between them. This requires a representation above the level of pixels that can beExpand
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Discriminative Probabilistic Models for Relational Data
In many supervised learning tasks, the entities to be labeled are related to each other in complex ways and their labels are not independent. For example, in hypertext classification, the labels ofExpand
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Toward Optimal Feature Selection
In this paper, we examine a method for feature subset selection based on Information Theory. Initially, a framework for defining the theoretically optimal, but computationally intractable, method forExpand
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