Above are papers related to the following paper: [Paper_ID] Paper #1107385. 	[Title] Using Mixtures of Factor Analyzers for Segmentation and Pose Estimation  Category: Visual Processing Preference: Oral [Abstract] To read a hand-written digit string, it is helpful to segment the image into separate digits. Bottom-up segmentation heuristics often fail when neighboring digits overlap substantially. We describe a system that has a stochastic generative model of each digit class and we show that this is the only knowledge required for segmentation. The system uses Gibbs sampling to construct a perceptual interpretation of a digit string and segmentation arises naturally from the "explaining away" effects that occur during Bayesian inference. By using conditional mixtures of factor analyzers, it is possible to extract an explicit, compact representation of the instantiation parameters that describe the pose of each digit. These instantiation parameters can then be used as the inputs to a higher level system that models the relationships between digits. The same technique could be used to model individual digits as redundancies between the instantiation parameters of their parts. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #12330. 	[Title] Recognizing Handwritten Digits Using Mixtures of Linear Models [Abstract] We construct a mixture of locally linear generative models of a collection of pixel-based images of digits, and use them for recognition. Different models of a given digit are used to capture different styles of writing, and new images are classified by evaluating their log-likelihoods under each model. We use an EM-based algorithm in which the M-step is computationally straightforward principal components analysis (PCA). Incorporating tangent-plane information [12] about expected local deformations only requires adding tangent vectors into the sample covariance matrices for the PCA, and it demonstrably improves performance. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1120777. 	[Title] Modelling the Manifolds of Images of Handwritten Digits [Abstract]  Please summarise all the given information.

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Paper #1107385 has the title `Using Mixtures of Factor Analyzers for Segmentation and Pose Estimation  Category: Visual Processing Preference: Oral  
` and the abstract `To read a hand-written digit string, it is helpful to segment the image into separate digits. Bottom-up segmentation heuristics often fail when neighboring digits overlap substantially. We describe a system that has a stochastic generative model of each digit class and we show that this is the only knowledge required for segmentation. The system uses Gibbs sampling to construct a perceptual interpretation of a digit string and segmentation arises naturally from the "explaining away" effects that occur during Bayesian inference. By using conditional mixtures of factor analyzers, it is possible to extract an explicit, compact representation of the instantiation parameters that describe the pose of each digit. These instantiation parameters can then be used as the inputs to a higher level system that models the relationships between digits. The same technique could be used to model individual digits as redundancies between the instantiation parameters of their parts.
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Question: Which category should Paper #1107385 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1107385 has the title `Using Mixtures of Factor Analyzers for Segmentation and Pose Estimation  Category: Visual Processing Preference: Oral  
` and the abstract `To read a hand-written digit string, it is helpful to segment the image into separate digits. Bottom-up segmentation heuristics often fail when neighboring digits overlap substantially. We describe a system that has a stochastic generative model of each digit class and we show that this is the only knowledge required for segmentation. The system uses Gibbs sampling to construct a perceptual interpretation of a digit string and segmentation arises naturally from the "explaining away" effects that occur during Bayesian inference. By using conditional mixtures of factor analyzers, it is possible to extract an explicit, compact representation of the instantiation parameters that describe the pose of each digit. These instantiation parameters can then be used as the inputs to a higher level system that models the relationships between digits. The same technique could be used to model individual digits as redundancies between the instantiation parameters of their parts.
`.
Question: Which category should Paper #1107385 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].

This paper should be classified into: Neural_Networks