Above are papers related to the following paper: [Paper_ID] Paper #1131420. 	[Title] VIEWNET ARCHITECTURES FOR INVARIANT 3-D OBJECT LEARNING AND RECOGNITION FROM MULTIPLE 2-D VIEWS [Abstract] 3 The recognition of 3-D objects from sequences of their 2-D views is modeled by a family of self-organizing neural architectures, called VIEWNET, that use View Information Encoded With NETworks. VIEWNET incorporates a preprocessor that generates a compressed but 2-D invariant representation of an image, a supervised incremental learning system (Fuzzy ARTMAP) that classifies the preprocessed representations into 2-D view categories whose outputs are combined into 3-D invariant object categories, and a working memory that makes a 3-D object prediction by accumulating evidence over time from 3-D object category nodes as multiple 2-D views are experienced. VIEWNET was benchmarked on an MIT Lincoln Laboratory database of 128x128 2-D views of aircraft, including small frontal views, with and without additive noise. A recognition rate of up to 90% is achieved with one 2-D view and of up to 98.5% correct with three 2-D views. The properties of 2-D view and 3-D object category nodes are compared with those of cells in monkey inferotemporal cortex. Please summarise all the given information.

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Paper #1131420 has the title `VIEWNET ARCHITECTURES FOR INVARIANT 3-D OBJECT LEARNING AND RECOGNITION FROM MULTIPLE 2-D VIEWS  
` and the abstract `3 The recognition of 3-D objects from sequences of their 2-D views is modeled by a family of self-organizing neural architectures, called VIEWNET, that use View Information Encoded With NETworks. VIEWNET incorporates a preprocessor that generates a compressed but 2-D invariant representation of an image, a supervised incremental learning system (Fuzzy ARTMAP) that classifies the preprocessed representations into 2-D view categories whose outputs are combined into 3-D invariant object categories, and a working memory that makes a 3-D object prediction by accumulating evidence over time from 3-D object category nodes as multiple 2-D views are experienced. VIEWNET was benchmarked on an MIT Lincoln Laboratory database of 128x128 2-D views of aircraft, including small frontal views, with and without additive noise. A recognition rate of up to 90% is achieved with one 2-D view and of up to 98.5% correct with three 2-D views. The properties of 2-D view and 3-D object category nodes are compared with those of cells in monkey inferotemporal cortex. 
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Question: Which category should Paper #1131420 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1131420 has the title `VIEWNET ARCHITECTURES FOR INVARIANT 3-D OBJECT LEARNING AND RECOGNITION FROM MULTIPLE 2-D VIEWS  
` and the abstract `3 The recognition of 3-D objects from sequences of their 2-D views is modeled by a family of self-organizing neural architectures, called VIEWNET, that use View Information Encoded With NETworks. VIEWNET incorporates a preprocessor that generates a compressed but 2-D invariant representation of an image, a supervised incremental learning system (Fuzzy ARTMAP) that classifies the preprocessed representations into 2-D view categories whose outputs are combined into 3-D invariant object categories, and a working memory that makes a 3-D object prediction by accumulating evidence over time from 3-D object category nodes as multiple 2-D views are experienced. VIEWNET was benchmarked on an MIT Lincoln Laboratory database of 128x128 2-D views of aircraft, including small frontal views, with and without additive noise. A recognition rate of up to 90% is achieved with one 2-D view and of up to 98.5% correct with three 2-D views. The properties of 2-D view and 3-D object category nodes are compared with those of cells in monkey inferotemporal cortex. 
`.
Question: Which category should Paper #1131420 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