Above are papers related to the following paper: [Paper_ID] Paper #252725. 	[Title] Learning Evolving Concepts Using Partial-Memory Approach  Machine Learning and Inference Laboratory [Abstract] This paper addresses the problem of learning evolving concepts, that is, concepts whose meaning gradually evolves in time. Solving this problem is important to many applications, for example, building intelligent agents for helping users in Internet search, active vision, automatically updating knowledge-bases, or acquiring profiles of users of telecommunication networks. Requirements for a learning architecture supporting such applications include the ability to incrementally modify concept definitions to accommodate new information, fast learning and recognition rates, low memory needs, and the understandability of computer-created concept descriptions. To address these requirements, we propose a learning architecture based on Variable-Valued Logic, the Star Methodology, and the AQ algorithm. The method uses a partial-memory approach, which means that in each step of learning, the system remembers the current concept descriptions and specially selected representative examples from the past experience. The developed method has been experimentally applied to the problem of computer system intrusion detection. The results show significant advantages of the method in learning speed and memory requirements with only slight decreases in predictive accuracy and concept simplicity when compared to traditional batch-style learning in which all training examples are provided at once. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1102794. 	[Title] A Method for Partial-Memory Incremental Learning and its Application to Computer Intrusion Detection Machine Learning [Abstract] This paper describes a partial-memory incremental learning method based on the AQ15c inductive learning system. The method maintains a representative set of past training examples that are used together with new examples to appropriately modify the currently held hypotheses. Incremental learning is evoked by feedback from the environment or from the user. Such a method is useful in applications involving intelligent agents acting in a changing environment, active vision, and dynamic knowledge-bases. For this study, the method is applied to the problem of computer intrusion detection in which symbolic profiles are learned for a computer systems users. In the experiments, the proposed method yielded significant gains in terms of learning time and memory requirements at the expense of slightly lower predictive accuracy and higher concept complexity, when compared to batch learning, in which all examples are given at once. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #9559. 	[Title] A Partial Memory Incremental Learning Methodology And Its Application To Computer Intrusion Detection [Abstract]  Please summarise all the given information.

Paper #252725 has the title `Learning Evolving Concepts Using Partial-Memory Approach  Machine Learning and Inference Laboratory  
` and the abstract `This paper addresses the problem of learning evolving concepts, that is, concepts whose meaning gradually evolves in time. Solving this problem is important to many applications, for example, building intelligent agents for helping users in Internet search, active vision, automatically updating knowledge-bases, or acquiring profiles of users of telecommunication networks. Requirements for a learning architecture supporting such applications include the ability to incrementally modify concept definitions to accommodate new information, fast learning and recognition rates, low memory needs, and the understandability of computer-created concept descriptions. To address these requirements, we propose a learning architecture based on Variable-Valued Logic, the Star Methodology, and the AQ algorithm. The method uses a partial-memory approach, which means that in each step of learning, the system remembers the current concept descriptions and specially selected representative examples from the past experience. The developed method has been experimentally applied to the problem of computer system intrusion detection. The results show significant advantages of the method in learning speed and memory requirements with only slight decreases in predictive accuracy and concept simplicity when compared to traditional batch-style learning in which all training examples are provided at once. 
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Question: Which category should Paper #252725 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #252725 has the title `Learning Evolving Concepts Using Partial-Memory Approach  Machine Learning and Inference Laboratory  
` and the abstract `This paper addresses the problem of learning evolving concepts, that is, concepts whose meaning gradually evolves in time. Solving this problem is important to many applications, for example, building intelligent agents for helping users in Internet search, active vision, automatically updating knowledge-bases, or acquiring profiles of users of telecommunication networks. Requirements for a learning architecture supporting such applications include the ability to incrementally modify concept definitions to accommodate new information, fast learning and recognition rates, low memory needs, and the understandability of computer-created concept descriptions. To address these requirements, we propose a learning architecture based on Variable-Valued Logic, the Star Methodology, and the AQ algorithm. The method uses a partial-memory approach, which means that in each step of learning, the system remembers the current concept descriptions and specially selected representative examples from the past experience. The developed method has been experimentally applied to the problem of computer system intrusion detection. The results show significant advantages of the method in learning speed and memory requirements with only slight decreases in predictive accuracy and concept simplicity when compared to traditional batch-style learning in which all training examples are provided at once. 
`.
Question: Which category should Paper #252725 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