Above are papers related to the following paper: [Paper_ID] Paper #145315. 	[Title] In: Machine Learning, Meta-reasoning and Logics, pp207-232,  Learning from Imperfect Data [Abstract] Systems interacting with real-world data must address the issues raised by the possible presence of errors in the observations it makes. In this paper we first present a framework for discussing imperfect data and the resulting problems it may cause. We distinguish between two categories of errors in data random errors or `noise', and systematic errors and examine their relationship to the task of describing observations in a way which is also useful for helping in future problem-solving and learning tasks. Secondly we proceed to examine some of the techniques currently used in AI research for recognising such errors. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #649944. 	[Title] Knowledge Integration and Learning [Abstract] LIACC - Technical Report 91-1 Abstract. In this paper we address the problem of acquiring knowledge by integration . Our aim is to construct an integrated knowledge base from several separate sources. The objective of integration is to construct one system that exploits all the knowledge that is available and has good performance. The aim of this paper is to discuss the methodology of knowledge integration and present some concrete results. In our experiments the performance of the integrated theory exceeded the performance of the individual theories by quite a significant amount. Also, the performance did not fluctuate much when the experiments were repeated. These results indicate knowledge integration can complement other existing ML methods. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #294126. 	[Title] Knowledge Acquisition via Knowledge Integration [Abstract] In this paper we are concerned with the problem of acquiring knowledge by integration. Our aim is to construct an integrated knowledge base from several separate sources. The need to merge knowledge bases can arise, for example, when knowledge bases are acquired independently from interactions with several domain experts. As opinions of different domain experts may differ, the knowledge bases constructed in this way will normally differ too. A similar problem can also arise whenever separate knowledge bases are generated by learning algorithms. The objective of integration is to construct one system that exploits all the knowledge that is available and has a good performance. The aim of this paper is to discuss the methodology of knowledge integration, describe the implemented system (INTEG.3), and present some concrete results which demonstrate the advantages of this method. Please summarise all the given information.

Paper #145315 has the title `In: Machine Learning, Meta-reasoning and Logics, pp207-232,  Learning from Imperfect Data  
` and the abstract `Systems interacting with real-world data must address the issues raised by the possible presence of errors in the observations it makes. In this paper we first present a framework for discussing imperfect data and the resulting problems it may cause. We distinguish between two categories of errors in data random errors or `noise', and systematic errors and examine their relationship to the task of describing observations in a way which is also useful for helping in future problem-solving and learning tasks. Secondly we proceed to examine some of the techniques currently used in AI research for recognising such errors.
`.
Question: Which category should Paper #145315 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #145315 has the title `In: Machine Learning, Meta-reasoning and Logics, pp207-232,  Learning from Imperfect Data  
` and the abstract `Systems interacting with real-world data must address the issues raised by the possible presence of errors in the observations it makes. In this paper we first present a framework for discussing imperfect data and the resulting problems it may cause. We distinguish between two categories of errors in data random errors or `noise', and systematic errors and examine their relationship to the task of describing observations in a way which is also useful for helping in future problem-solving and learning tasks. Secondly we proceed to examine some of the techniques currently used in AI research for recognising such errors.
`.
Question: Which category should Paper #145315 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