Above are papers related to the following paper: [Paper_ID] Paper #1125953. 	[Title] Language Series Revisited: The Complexity of Hypothesis Spaces in ILP [Abstract] Restrictions on the number and depth of existential variables as defined in the language series of Clint [Rae92] are widely used in ILP and expected to produce a considerable reduction in the size of the hypothesis space. In this paper we show that this is generally not the case. The lower bounds we present lead to intractable hypothesis spaces except for toy domains. We argue that the parameters chosen in Clint are unsuitable for sensible bias shift operations, and propose alternative approaches resulting in the desired reduction of the hypothesis space and allowing for a natural integration of the shift of bias. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1272. 	[Title] Learning a set of primitive actions with an Induction of decision trees. Machine Learning, 1(1):81-106, [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #520471. 	[Title] The Arguments of Newly Invented Predicates in ILP [Abstract] In this paper we investigate the problem of choosing arguments for a new predicate. We identify the relevant terms to be considered as arguments, and propose methods to choose among them based on propositional minimisation. Please summarise all the given information.

Paper #1125953 has the title `Language Series Revisited: The Complexity of Hypothesis Spaces in ILP  
` and the abstract `Restrictions on the number and depth of existential variables as defined in the language series of Clint [Rae92] are widely used in ILP and expected to produce a considerable reduction in the size of the hypothesis space. In this paper we show that this is generally not the case. The lower bounds we present lead to intractable hypothesis spaces except for toy domains. We argue that the parameters chosen in Clint are unsuitable for sensible bias shift operations, and propose alternative approaches resulting in the desired reduction of the hypothesis space and allowing for a natural integration of the shift of bias.
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Question: Which category should Paper #1125953 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1125953 has the title `Language Series Revisited: The Complexity of Hypothesis Spaces in ILP  
` and the abstract `Restrictions on the number and depth of existential variables as defined in the language series of Clint [Rae92] are widely used in ILP and expected to produce a considerable reduction in the size of the hypothesis space. In this paper we show that this is generally not the case. The lower bounds we present lead to intractable hypothesis spaces except for toy domains. We argue that the parameters chosen in Clint are unsuitable for sensible bias shift operations, and propose alternative approaches resulting in the desired reduction of the hypothesis space and allowing for a natural integration of the shift of bias.
`.
Question: Which category should Paper #1125953 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: Rule_Learning