Above are papers related to the following paper: [Paper_ID] Paper #1108841. 	[Title] Segmentation and Classification of Combined Optical and Radar Imagery [Abstract] The classification performance of a neural network for combined six-band Landsat-TM and one-band ERS-1/SAR PRI imagery from the same scene is carried out. Different combinations of the data | either raw, segmented or filtered |, using the available ground truth polygons, training and test sets are created. The training sets are used for learning while the test sets are used for verification of the neural network. The different combinations are evaluated here. Please summarise all the given information.

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Paper #1108841 has the title `Segmentation and Classification of Combined Optical and Radar Imagery  
` and the abstract `The classification performance of a neural network for combined six-band Landsat-TM and one-band ERS-1/SAR PRI imagery from the same scene is carried out. Different combinations of the data | either raw, segmented or filtered |, using the available ground truth polygons, training and test sets are created. The training sets are used for learning while the test sets are used for verification of the neural network. The different combinations are evaluated here. 
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Question: Which category should Paper #1108841 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1108841 has the title `Segmentation and Classification of Combined Optical and Radar Imagery  
` and the abstract `The classification performance of a neural network for combined six-band Landsat-TM and one-band ERS-1/SAR PRI imagery from the same scene is carried out. Different combinations of the data | either raw, segmented or filtered |, using the available ground truth polygons, training and test sets are created. The training sets are used for learning while the test sets are used for verification of the neural network. The different combinations are evaluated here. 
`.
Question: Which category should Paper #1108841 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