Above are papers related to the following paper: [Paper_ID] Paper #37998. 	[Title] Combining Top-down and Bottom-up Techniques in Inductive Logic Programming [Abstract] This paper describes a new method for inducing logic programs from examples which attempts to integrate the best aspects of existing ILP methods into a single coherent framework. In particular, it combines a bottom-up method similar to Golem with a top-down method similar to Foil. It also includes a method for predicate invention similar to Champ and an elegant solution to the "noisy oracle" problem which allows the system to learn recursive programs without requiring a complete set of positive examples. Systematic experimental comparisons to both Golem and Foil on a range of problems are used to clearly demonstrate the ad vantages of the approach. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1110531. 	[Title] Computational Learning in Humans and Machines [Abstract] In this paper we review research on machine learning and its relation to computational models of human learning. We focus initially on concept induction, examining five main approaches to this problem, then consider the more complex issue of learning sequential behaviors. After this, we compare the rhetoric that sometimes appears in the machine learning and psychological literature with the growing evidence that different theoretical paradigms typically produce similar results. In response, we suggest that concrete computational models, which currently dominate the field, may be less useful than simulations that operate at a more abstract level. We illustrate this point with an abstract simulation that explains a challenging phenomenon in the area of category learning, and we conclude with some general observations about such abstract models. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #38000. 	[Title] on Inductive Logic Programming (ILP-95) Inducing Logic Programs without Explicit Negative Examples [Abstract] This paper presents a method for learning logic programs without explicit negative examples by exploiting an assumption of output completeness. A mode declaration is supplied for the target predicate and each training input is assumed to be accompanied by all of its legal outputs. Any other outputs generated by an incomplete program implicitly represent negative examples; however, large numbers of ground negative examples never need to be generated. This method has been incorporated into two ILP systems, Chillin and IFoil, both of which use intensional background knowledge. Tests on two natural language acquisition tasks, case-role mapping and past-tense learning, illustrate the advantages of the approach. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #23507. 	[Title] Natural Language Processing with Subsymbolic Neural Networks [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #13717. 	[Title] Learning Semantic Grammars with Constructive Inductive Logic Programming [Abstract] Automating the construction of semantic grammars is a difficult and interesting problem for machine learning. This paper shows how the semantic-grammar acquisition problem can be viewed as the learning of search-control heuristics in a logic program. Appropriate control rules are learned using a new first-order induction algorithm that automatically invents useful syntactic and semantic categories. Empirical results show that the learned parsers generalize well to novel sentences and out-perform previous approaches based on connectionist techniques. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #63931. 	[Title] Combining FOIL and EBG to Speed-up Logic Programs [Abstract] This paper presents an algorithm that combines traditional EBL techniques and recent developments in inductive logic programming to learn effective clause selection rules for Prolog programs. When these control rules are incorporated into the original program, significant speed-up may be achieved. The algorithm is shown to be an improvement over competing EBL approaches in several domains. Additionally, the algorithm is capable of automatically transforming some intractable algorithms into ones that run in polynomial time. Please summarise all the given information.

Paper #37998 has the title `Combining Top-down and Bottom-up Techniques in Inductive Logic Programming  
` and the abstract `This paper describes a new method for inducing logic programs from examples which attempts to integrate the best aspects of existing ILP methods into a single coherent framework. In particular, it combines a bottom-up method similar to Golem with a top-down method similar to Foil. It also includes a method for predicate invention similar to Champ and an elegant solution to the "noisy oracle" problem which allows the system to learn recursive programs without requiring a complete set of positive examples. Systematic experimental comparisons to both Golem and Foil on a range of problems are used to clearly demonstrate the ad vantages of the approach.
`.
Question: Which category should Paper #37998 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #37998 has the title `Combining Top-down and Bottom-up Techniques in Inductive Logic Programming  
` and the abstract `This paper describes a new method for inducing logic programs from examples which attempts to integrate the best aspects of existing ILP methods into a single coherent framework. In particular, it combines a bottom-up method similar to Golem with a top-down method similar to Foil. It also includes a method for predicate invention similar to Champ and an elegant solution to the "noisy oracle" problem which allows the system to learn recursive programs without requiring a complete set of positive examples. Systematic experimental comparisons to both Golem and Foil on a range of problems are used to clearly demonstrate the ad vantages of the approach.
`.
Question: Which category should Paper #37998 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