Above are papers related to the following paper: [Paper_ID] Paper #1114838. 	[Title] Efficient Construction of Networks for Learned Representations with General to Specific Relationships [Abstract] Machine learning systems often represent concepts or rules as sets of attribute-value pairs. Many learning algorithms generalize or specialize these concept representations by removing or adding pairs. Thus concepts are created that have general to specific relationships. This paper presents algorithms to connect concepts into a network based on their general to specific relationships. Since any concept can access related concepts quickly, the resulting structure allows increased efficiency in learning and reasoning. The time complexity of one set of learning models improves from O(n log n) to O(log n) (where n is the number of nodes) when using the general to specific structure. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #270600. 	[Title] GENERALIZATION BY CONTROLLED EXPANSION OF EXAMPLES [Abstract] SG (Specific to General) is a learning system that derives general rules from specific examples. SG learns incrementally with good speed and generalization. The SG network is built of many simple nodes that adapt to the problem being learned. Learning is done without requiring user adjustment of sensitive parameters and noise is tolerated with graceful degradation in performance. Nodes learn important features in the input space and then monitor the ability of the features to predict output values. Learning is O(n log n) for each example, where n is the number of nodes in the network, and the number of inputs and output values are treated as constants. An enhanced network topology reduces time complexity to O(log n). Empirical results show that the model gives good generalization and that learning converges in a small number of training passes. Please summarise all the given information.

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Paper #1114838 has the title `Efficient Construction of Networks for Learned Representations with General to Specific Relationships  
` and the abstract `Machine learning systems often represent concepts or rules as sets of attribute-value pairs. Many learning algorithms generalize or specialize these concept representations by removing or adding pairs. Thus concepts are created that have general to specific relationships. This paper presents algorithms to connect concepts into a network based on their general to specific relationships. Since any concept can access related concepts quickly, the resulting structure allows increased efficiency in learning and reasoning. The time complexity of one set of learning models improves from O(n log n) to O(log n) (where n is the number of nodes) when using the general to specific structure. 
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Question: Which category should Paper #1114838 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1114838 has the title `Efficient Construction of Networks for Learned Representations with General to Specific Relationships  
` and the abstract `Machine learning systems often represent concepts or rules as sets of attribute-value pairs. Many learning algorithms generalize or specialize these concept representations by removing or adding pairs. Thus concepts are created that have general to specific relationships. This paper presents algorithms to connect concepts into a network based on their general to specific relationships. Since any concept can access related concepts quickly, the resulting structure allows increased efficiency in learning and reasoning. The time complexity of one set of learning models improves from O(n log n) to O(log n) (where n is the number of nodes) when using the general to specific structure. 
`.
Question: Which category should Paper #1114838 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: Theory