Above are papers related to the following paper: [Paper_ID] Paper #6318. 	[Title] Rule Induction and Instance-Based Learning: A Unified Approach [Abstract] This paper presents a new approach to inductive learning that combines aspects of instance-based learning and rule induction in a single simple algorithm. The RISE system searches for rules in a specific-to-general fashion, starting with one rule per training example, and avoids some of the difficulties of separate-and-conquer approaches by evaluating each proposed induction step globally, i.e., through an efficient procedure that is equivalent to checking the accuracy of the rule set as a whole on every training example. Classification is performed using a best-match strategy, and reduces to nearest-neighbor if all generalizations of instances were rejected. An extensive empirical study shows that RISE consistently achieves higher accuracies than state-of-the-art representatives of its "parent" paradigms (PEBLS and CN2), and also outperforms a decision-tree learner (C4.5) in 13 out of 15 test domains (in Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1121057. 	[Title] Inductive CBR for Customer Support [Abstract] Over the past few years, the telecommunications paradigm has been shifting rapidly from hardware to middleware. In particular, the traditional issues of service characteristics and network control are being replaced by the modern, customer-driven issues of network and service management (e.g., electronic commerce, one-stop shops). An area of service management which has extremely high visibility and negative impact when managed badly is that of problem handling. Problem handling is a very knowledge intensive activity, particularly nowadays with the increase in number and complexity of services becoming available. Trials at several BT support centres have already demonstrated the potential of case-based reasoning technology in improving current practice for problem detection and diagnosis. A major cost involved in implementing a case-based system is in the manual building of the initial case base and then in the subsequent maintenance of that case base over time. This paper shows how inductive machine learning can be combined with case-based reasoning to produce an intelligent system capable of both extracting knowledge from raw data automatically and reasoning from that knowledge. In addition to discovering knowledge in existing data repositories, the integrated system may be used to acquire and revise knowledge continually. Experiments with the suggested integrated approach demonstrate promise and justify the next step. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #20833. 	[Title] Using Partitioning to Speed Up Specific-to-General Rule Induction [Abstract] RISE (Domingos 1995; in press) is a rule induction algorithm that proceeds by gradually generalizing rules, starting with one rule per example. This has several advantages compared to the more common strategy of gradually specializing initially null rules, and has been shown to lead to significant accuracy gains over algorithms like C4.5RULES and CN2 in a large number of application domains. However, RISE's running time (like that of other rule induction algorithms) is quadratic in the number of examples, making it unsuitable for processing very large databases. This paper studies the use of partitioning to speed up RISE, and compares it with the well-known method of windowing. The use of partitioning in a specific-to-general induction setting creates synergies that would not be possible with a general-to-specific system. Partitioning often reduces running time and improves accuracy at the same time. In noisy conditions, the performance of windowing deteriorates rapidly, while that of partitioning remains stable. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #63812. 	[Title] FLARE: Induction with Prior Knowledge [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #11335. 	[Title] Concept Learning and the Problem of Small [Abstract]  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 #1481. 	[Title] (1994); Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. Case-Based Reasoning: Foundational Issues, Methodological [Abstract] Case-based reasoning is a recent approach to problem solving and learning that has got a lot of attention over the last few years. Originating in the US, the basic idea and underlying theories have spread to other continents, and we are now within a period of highly active research in case-based reasoning in Europe, as well. This paper gives an overview of the foundational issues related to case-based reasoning, describes some of the leading methodological approaches within the field, and exemplifies the current state through pointers to some systems. Initially, a general framework is defined, to which the subsequent descriptions and discussions will refer. The framework is influenced by recent methodologies for knowledge level descriptions of intelligent systems. The methods for case retrieval, reuse, solution testing, and learning are summarized, and their actual realization is discussed in the light of a few example systems that represent different CBR approaches. We also discuss the role of case-based methods as one type of reasoning and learning method within an integrated system architecture. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1108656. 	[Title] Rule Generation and Compaction in the wwtp [Abstract] In this paper we discuss our approach to learning classification rules from data. We sketch out two modules of our architecture, namely LINNEO + and GAR. LINNEO + , which is a knowledge acquisition tool for ill-structured domains automatically generating classes from examples that incrementally works with an unsupervised strategy. LINNEO + 's output, a representation of the conceptual structure of the domain in terms of classes, is the input to GAR that is used to generate a set of classification rules for the original training set. GAR can generate both conjunctive and disjunctive rules. Herein we present an application of these techniques to data obtained from a real wastewater treatment plant in order to help the construction of a rule base. This rule will be used for a knowledge-based system that aims to supervise the whole process. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #135798. 	[Title] LINNEO A Classification Methodology for Ill-structured Domains [Abstract] In this work we present a classification methodology (LINNEO + ) to discover concepts from ill-structured domains and to organize hierarchies with them. In order to achieve this aim LINNEO + uses conceptual learning techniques and classification. The final target is to build knowledge bases after expert validation. Some techniques for the improvement of the results in the classification step are used, like biasing using partial expert knowledge (classification rules or causal and structural dependencies between attributes) or delayed cluster assignation of objects. Also some comparisons with a few well-known systems are shown. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1119295. 	[Title] Distance Induction in First Order Logic used for classification via a k-nearest-neighbor process. Experiments on [Abstract] This paper tackles the supervised induction of a distance from examples described as Horn clauses or constrained clauses. In opposition to syntax-driven approaches, this approach is discrimination-driven: it proceeds by defining a small set of complex discriminant hypotheses. These hypotheses serve as new concepts, used to redescribe the initial examples. Further, this redescription can be embedded into the space of natural integers, and a distance between examples thus naturally follows. Please summarise all the given information.

Paper #6318 has the title `Rule Induction and Instance-Based Learning: A Unified Approach  
` and the abstract `This paper presents a new approach to inductive learning that combines aspects of instance-based learning and rule induction in a single simple algorithm. The RISE system searches for rules in a specific-to-general fashion, starting with one rule per training example, and avoids some of the difficulties of separate-and-conquer approaches by evaluating each proposed induction step globally, i.e., through an efficient procedure that is equivalent to checking the accuracy of the rule set as a whole on every training example. Classification is performed using a best-match strategy, and reduces to nearest-neighbor if all generalizations of instances were rejected. An extensive empirical study shows that RISE consistently achieves higher accuracies than state-of-the-art representatives of its "parent" paradigms (PEBLS and CN2), and also outperforms a decision-tree learner (C4.5) in 13 out of 15 test domains (in 
`.
Question: Which category should Paper #6318 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #6318 has the title `Rule Induction and Instance-Based Learning: A Unified Approach  
` and the abstract `This paper presents a new approach to inductive learning that combines aspects of instance-based learning and rule induction in a single simple algorithm. The RISE system searches for rules in a specific-to-general fashion, starting with one rule per training example, and avoids some of the difficulties of separate-and-conquer approaches by evaluating each proposed induction step globally, i.e., through an efficient procedure that is equivalent to checking the accuracy of the rule set as a whole on every training example. Classification is performed using a best-match strategy, and reduces to nearest-neighbor if all generalizations of instances were rejected. An extensive empirical study shows that RISE consistently achieves higher accuracies than state-of-the-art representatives of its "parent" paradigms (PEBLS and CN2), and also outperforms a decision-tree learner (C4.5) in 13 out of 15 test domains (in 
`.
Question: Which category should Paper #6318 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: Case_Based