Above are papers related to the following paper: [Paper_ID] Paper #67246. 	[Title] VISOR: Schema-based Scene Analysis with Structured Neural Networks [Abstract] A novel approach to object recognition and scene analysis based on neural network representation of visual schemas is described. Given an input scene, the VISOR system focuses attention successively at each component, and the schema representations cooperate and compete to match the inputs. The schema hierarchy is learned from examples through unsupervised adaptation and reinforcement learning. VISOR learns that some objects are more important than others in identifying the scene, and that the importance of spatial relations varies depending on the scene. As the inputs differ increasingly from the schemas, VISOR's recognition process is remarkably robust, and automatically generates a measure of confidence in the analysis. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #67245. 	[Title] Priming, Perceptual Reversal, and Circular Reaction in a Neural Network Model of Schema-Based Vision [Abstract] VISOR is a neural network system for object recognition and scene analysis that learns visual schemas from examples. Processing in VISOR is based on cooperation, competition, and parallel bottom-up and top-down activation of schema representations. Similar principles appear to underlie much of human visual processing, and VISOR can therefore be used to model various perceptual phenomena. This paper focuses on analyzing three phenomena through simulation with VISOR: (1) priming and mental imagery, (2) perceptual reversal, and (3) circular reaction. The results illustrate similarity and subtle differences between the mechanisms mediating priming and mental imagery, show how the two opposing accounts of perceptual reversal (neural satiation and cognitive factors) may both contribute to the phenomenon, and demonstrate how intentional actions can be gradually learned from reflex actions. Successful simulation of such effects suggests that similar mechanisms may govern human visual perception and learning of visual schemas. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #32083. 	[Title] Back Propagation is Sensitive to Initial Conditions [Abstract] This paper explores the effect of initial weight selection on feed-forward networks learning simple functions with the back-propagation technique. We first demonstrate, through the use of Monte Carlo techniques, that the magnitude of the initial condition vector (in weight space) is a very significant parameter in convergence time variability. In order to further understand this result, additional deterministic experiments were performed. The results of these experiments demonstrate the extreme sensitivity of back propagation to initial weight configuration. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #31769. 	[Title] Representing and Learning Visual Schemas in Neural Networks for Scene Analysis [Abstract] Using scene analysis as the task, this research focuses on three fundamental problems in neural network systems: (1) limited processing resources, (2) representing schemas, and (3) learning schemas. The first problem arises because no practical neural network can process all the visual input simultaneously and efficiently. The solution is to process a small amount of the input in parallel, and successively focus on the other parts of the input. This strategy requires that the system maintains structured knowledge for describing and interpreting the gathered information. The system should also learn to represent structured knowledge from examples of objects and scenes. VISOR, the system described in this paper, consists of three main components. The Low-Level Visual Module (simulated using procedural programs) extracts featural and positional information from the visual input. The Schema Module encodes structured knowledge about possible objects, and provides top-down information for the Low-Level Visual Module to focus attention at different parts of the scene. The Response Module learns to associate the schema activation patterns with external responses. It enables the external environment to provide reinforcement feedback for the learning of schematic structures. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #3229. 	[Title] Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2 [Abstract] Neural computation, also called connectionism, parallel distributed processing, neural network modeling or brain-style computation, has grown rapidly in the last decade. Despite this explosion, and ultimately because of impressive applications, there has been a dire need for a concise introduction from a theoretical perspective, analyzing the strengths and weaknesses of connectionist approaches and establishing links to other disciplines, such as statistics or control theory. The Introduction to the Theory of Neural Computation by Hertz, Krogh and Palmer (subsequently referred to as HKP) is written from the perspective of physics, the home discipline of the authors. The book fulfills its mission as an introduction for neural network novices, provided that they have some background in calculus, linear algebra, and statistics. It covers a number of models that are often viewed as disjoint. Critical analyses and fruitful comparisons between these models Please summarise all the given information.

Paper #67246 has the title `VISOR: Schema-based Scene Analysis with Structured Neural Networks  
` and the abstract `A novel approach to object recognition and scene analysis based on neural network representation of visual schemas is described. Given an input scene, the VISOR system focuses attention successively at each component, and the schema representations cooperate and compete to match the inputs. The schema hierarchy is learned from examples through unsupervised adaptation and reinforcement learning. VISOR learns that some objects are more important than others in identifying the scene, and that the importance of spatial relations varies depending on the scene. As the inputs differ increasingly from the schemas, VISOR's recognition process is remarkably robust, and automatically generates a measure of confidence in the analysis.
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Question: Which category should Paper #67246 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #67246 has the title `VISOR: Schema-based Scene Analysis with Structured Neural Networks  
` and the abstract `A novel approach to object recognition and scene analysis based on neural network representation of visual schemas is described. Given an input scene, the VISOR system focuses attention successively at each component, and the schema representations cooperate and compete to match the inputs. The schema hierarchy is learned from examples through unsupervised adaptation and reinforcement learning. VISOR learns that some objects are more important than others in identifying the scene, and that the importance of spatial relations varies depending on the scene. As the inputs differ increasingly from the schemas, VISOR's recognition process is remarkably robust, and automatically generates a measure of confidence in the analysis.
`.
Question: Which category should Paper #67246 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