Above are papers related to the following paper: [Paper_ID] Paper #562940. 	[Title] What online Machine Learning can do for Knowledge Acquisition A Case Study [Abstract] This paper reports on the development of a realistic knowledge-based application using the MOBAL system. Some problems and requirements resulting from industrial-caliber tasks are formulated. A step-by-step account of the construction of a knowledge base for such a task demonstrates how the interleaved use of several learning algorithms in concert with an inference engine and a graphical interface can fulfill those requirements. Design, analysis, revision, refinement and extension of a working model are combined in one incremental process. This illustrates the balanced cooperative modeling approach. The case study is taken from the telecommunications domain and more precisely deals with security management in telecommunications networks. MOBAL would be used as part of a security management tool for acquiring, validating and refining a security policy. The modeling approach is compared with other approaches, such as KADS and stand-alone machine learning. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1127619. 	[Title] Cooperation of Data-driven and Model-based Induction Methods for Relational Learning [Abstract] Inductive learning in relational domains has been shown to be intractable in general. Many approaches to this task have been suggested nevertheless; all in some way restrict the hypothesis space searched. They can be roughly divided into two groups: data-driven, where the restriction is encoded into the algorithm, and model-based, where the restrictions are made more or less explicit with some form of declarative bias. This paper describes Incy, an inductive learner that seeks to combine aspects of both approaches. Incy is initially data-driven, using examples and background knowledge to put forth and specialize hypotheses based on the "connectivity" of the data at hand. It is model-driven in that hypotheses are abstracted into rule models, which are used both for control decisions in the data-driven phase and for model-guided induction. Key Words: Inductive learning in relational domains, cooperation of data-driven and model-guided methods, implicit and declarative bias. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #2440. 	[Title] Quinlan, 1990 J.R. Quinlan. Learning logical definitions from relations. Machine Learning, First-order theory revision. In [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #212777. 	[Title] Inductive Learning of Characteristic Concept Descriptions from Small Sets of Classified Examples [Abstract] This paper presents a novel idea to the problem of learning concept descriptions from examples. Whereas most existing approaches rely on a large number of classified examples, the approach presented in the paper is aimed at being applicable when only a few examples are classified as positive (and negative) instances of a concept. The approach tries to take advantage of the information which can be induced from descriptions of unclassified objects using a conceptual clustering algorithm. The system Cola is described and results of applying Cola in two real-world domains are presented. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #61417. 	[Title] An Efficient Subsumption Algorithm for Inductive Logic Programming [Abstract] In this paper we investigate the efficiency of - subsumption (` ), the basic provability relation in ILP. As D ` C is NP-complete even if we restrict ourselves to linked Horn clauses and fix C to contain only a small constant number of literals, we investigate in several restrictions of D. We first adapt the notion of determinate clauses used in ILP and show that -subsumption is decidable in polynomial time if D is determinate with respect to C. Secondly, we adapt the notion of k-local Horn clauses and show that - subsumption is efficiently computable for some reasonably small k. We then show how these results can be combined, to give an efficient reasoning procedure for determinate k-local Horn clauses, an ILP-problem recently suggested to be polynomial predictable by Cohen (1993) by a simple counting argument. We finally outline how the -reduction algorithm, an essential part of every lgg ILP-learning algorithm, can be im proved by these ideas. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1106764. 	[Title] Efficient Algorithms for -Subsumption [Abstract] subsumption is a decidable but incomplete approximation of logic implication, important to inductive logic programming and theorem proving. We show that by context based elimination of possible matches a certain superset of the determinate clauses can be tested for subsumption in polynomial time. We discuss the relation between subsumption and the clique problem, showing in particular that using additional prior knowledge about the substitution space only a small fraction of the search space can be identified as possibly containing globally consistent solutions, which leads to an effective pruning rule. We present empirical results, demonstrating that a combination of both of the above approaches provides an extreme reduction of computational effort. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #94416. 	[Title] Efficient -Subsumption based on Graph Algorithms [Abstract] The -subsumption problem is crucial to the efficiency of ILP learning systems. We discuss two -subsumption algorithms based on strategies for preselecting suitable matching literals. The class of clauses, for which subsumption becomes polynomial, is a superset of the deterministic clauses. We further map the general problem of -subsumption to a certain problem of finding a clique of fixed size in a graph, and in return show that a specialization of the pruning strategy of the Car-raghan and Pardalos clique algorithm provides a dramatic reduction of the subsumption search space. We also present empirical results for the mesh design data set. Please summarise all the given information.

Paper #562940 has the title `What online Machine Learning can do for Knowledge Acquisition A Case Study  
` and the abstract `This paper reports on the development of a realistic knowledge-based application using the MOBAL system. Some problems and requirements resulting from industrial-caliber tasks are formulated. A step-by-step account of the construction of a knowledge base for such a task demonstrates how the interleaved use of several learning algorithms in concert with an inference engine and a graphical interface can fulfill those requirements. Design, analysis, revision, refinement and extension of a working model are combined in one incremental process. This illustrates the balanced cooperative modeling approach. The case study is taken from the telecommunications domain and more precisely deals with security management in telecommunications networks. MOBAL would be used as part of a security management tool for acquiring, validating and refining a security policy. The modeling approach is compared with other approaches, such as KADS and stand-alone machine learning. 
`.
Question: Which category should Paper #562940 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #562940 has the title `What online Machine Learning can do for Knowledge Acquisition A Case Study  
` and the abstract `This paper reports on the development of a realistic knowledge-based application using the MOBAL system. Some problems and requirements resulting from industrial-caliber tasks are formulated. A step-by-step account of the construction of a knowledge base for such a task demonstrates how the interleaved use of several learning algorithms in concert with an inference engine and a graphical interface can fulfill those requirements. Design, analysis, revision, refinement and extension of a working model are combined in one incremental process. This illustrates the balanced cooperative modeling approach. The case study is taken from the telecommunications domain and more precisely deals with security management in telecommunications networks. MOBAL would be used as part of a security management tool for acquiring, validating and refining a security policy. The modeling approach is compared with other approaches, such as KADS and stand-alone machine learning. 
`.
Question: Which category should Paper #562940 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