Above are papers related to the following paper: [Paper_ID] Paper #216878. 	[Title] Pattern Theoretic Feature Extraction and Constructive Induction [Abstract] This paper offers a perspective on features and pattern finding in general. This perspective is based on a robust complexity measure called Decomposed Function Car-dinality. A function decomposition algorithm for minimizing this complexity measure and finding the associated features is outlined. Results from experiments with this algorithm are also summarized. Please summarise all the given information.

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Paper #216878 has the title `Pattern Theoretic Feature Extraction and Constructive Induction  
` and the abstract `This paper offers a perspective on features and pattern finding in general. This perspective is based on a robust complexity measure called Decomposed Function Car-dinality. A function decomposition algorithm for minimizing this complexity measure and finding the associated features is outlined. Results from experiments with this algorithm are also summarized.
`.
Question: Which category should Paper #216878 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #216878 has the title `Pattern Theoretic Feature Extraction and Constructive Induction  
` and the abstract `This paper offers a perspective on features and pattern finding in general. This perspective is based on a robust complexity measure called Decomposed Function Car-dinality. A function decomposition algorithm for minimizing this complexity measure and finding the associated features is outlined. Results from experiments with this algorithm are also summarized.
`.
Question: Which category should Paper #216878 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