Above are papers related to the following paper: [Paper_ID] Paper #22883. 	[Title] On the Informativeness of the DNA Promoter Sequences Domain Theory [Abstract] The DNA promoter sequences domain theory and database have become popular for testing systems that integrate empirical and analytical learning. This note reports a simple change and reinterpretation of the domain theory in terms of M-of-N concepts, involving no learning, that results in an accuracy of 93.4% on the 106 items of the database. Moreover, an exhaustive search of the space of M-of-N domain theory interpretations indicates that the expected accuracy of a randomly chosen interpretation is 76.5%, and that a maximum accuracy of 97.2% is achieved in 12 cases. This demonstrates the informativeness of the domain theory, without the complications of understanding the interactions between various learning algorithms and the theory. In addition, our results help characterize the difficulty of learning using the DNA promoters theory. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #31043. 	[Title] FONN: Combining First Order Logic with Connectionist Learning [Abstract] This paper presents a neural network architecture that can manage structured data and refine knowledge bases expressed in a first order logic language. The presented framework is well suited to classification problems in which concept de scriptions depend upon numerical features of the data. In fact, the main goal of the neural architecture is that of refining the numerical part of the knowledge base, without changing its structure. In particular, we discuss a method to translate a set of classification rules into neural computation units. Here, we focus our attention on the translation method and on algorithms to refine network weights on struc tured data. The classification theory to be refined can be manually handcrafted or automatically acquired by a symbolic relational learning system able to deal with numerical features. As a matter of fact, the primary goal is to bring into a neural network architecture the capability of dealing with structured data of unrestricted size, by allowing to dynamically bind the classification rules to different items occur ring in the input data. An extensive experimentation on a challenging artificial case study shows that the network converges quite fastly and generalizes much better than propositional learners on an equivalent task definition. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #51909. 	[Title] Automated Refinement of First-Order Horn-Clause Domain Theories [Abstract] Knowledge acquisition is a difficult, error-prone, and time-consuming task. The task of automatically improving an existing knowledge base using learning methods is addressed by the class of systems performing theory refinement. This paper presents a system, Forte (First-Order Revision of Theories from Examples), which refines first-order Horn-clause theories by integrating a variety of different revision techniques into a coherent whole. Forte uses these techniques within a hill-climbing framework, guided by a global heuristic. It identifies possible errors in the theory and calls on a library of operators to develop possible revisions. The best revision is implemented, and the process repeats until no further revisions are possible. Operators are drawn from a variety of sources, including propositional theory refinement, first-order induction, and inverse resolution. Forte is demonstrated in several domains, including logic programming and qualitative modelling. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1119078. 	[Title] Tractability of Theory Patching [Abstract] In this paper we consider the problem of theory patching, in which we are given a domain theory, some of whose components are indicated to be possibly flawed, and a set of labeled training examples for the domain concept. The theory patching problem is to revise only the indicated components of the theory, such that the resulting theory correctly classifies all the training examples. Theory patching is thus a type of theory revision in which revisions are made to individual components of the theory. Our concern in this paper is to determine for which classes of logical domain theories the theory patching problem is tractable. We consider both propositional and first-order domain theories, and show that the theory patching problem is equivalent to that of determining what information contained in a theory is stable regardless of what revisions might be performed to the theory. We show that determining stability is tractable if the input theory satisfies two conditions: that revisions to each theory component have monotonic effects on the classification of examples, and that theory components act independently in the classification of examples in the theory. We also show how the concepts introduced can be used to determine the soundness and completeness of particular theory patching algorithms. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #51879. 	[Title] Combining Symbolic and Connectionist Learning Methods to Refine Certainty-Factor Rule-Bases [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1114664. 	[Title] Knowledge Based Systems [Abstract] Technical Report No. 95/2 Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #22874. 	[Title] A Similarity-Based Retrieval Tool for Software Repositories [Abstract] In this paper we present a prototype of a flexible similarity-based retrieval system. Its flexibility is supported by allowing for an imprecisely specified query. Moreover, our algorithm allows for assessing if the retrieved items are relevant in the initial context, specified in the query. The presented system can be used as a supporting tool for a software repository. We also discuss system evaluation with concerns on usefulness, scalability, applicability and comparability. Evaluation of the T A3 system on three domains gives us encouraging results and an integration of TA3 into a real software repository as a retrieval tool is ongoing. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #27535. 	[Title] Combining Connectionist and Symbolic Learning to Refine Certainty-Factor Rule Bases [Abstract] This paper describes Rapture | a system for revising probabilistic knowledge bases that combines connectionist and symbolic learning methods. Rapture uses a modified version of backpropagation to refine the certainty factors of a probabilistic rule base and it uses ID3's information-gain heuristic to add new rules. Results on refining three actual expert knowledge bases demonstrate that this combined approach generally performs better than previous methods. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #34961. 	[Title] Comparing Methods for Refining Certainty-Factor Rule-Bases [Abstract] This paper compares two methods for refining uncertain knowledge bases using propositional certainty-factor rules. The first method, implemented in the Rapture system, employs neural-network training to refine the certainties of existing rules but uses a symbolic technique to add new rules. The second method, based on the one used in the Kbann system, initially adds a complete set of potential new rules with very low certainty and allows neural-network training to filter and adjust these rules. Experimental results indicate that the former method results in significantly faster training and produces much simpler refined rule bases with slightly greater accuracy. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #22869. 	[Title] Applying Case-Based Reasoning to Control in Robotics [Abstract] The proposed architecture is experimentally evaluated on two real world domains and the results are compared to other machine learning algorithms applied to the same problem. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #51866. 	[Title] Bias-Driven Revision of Logical Domain Theories [Abstract] The theory revision problem is the problem of how best to go about revising a deficient domain theory using information contained in examples that expose inaccuracies. In this paper we present our approach to the theory revision problem for propositional domain theories. The approach described here, called PTR, uses probabilities associated with domain theory elements to numerically track the ``ow'' of proof through the theory. This allows us to measure the precise role of a clause or literal in allowing or preventing a (desired or undesired) derivation for a given example. This information is used to efficiently locate and repair awed elements of the theory. PTR is proved to converge to a theory which correctly classifies all examples, and shown experimentally to be fast and accurate even for deep theories. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #22875. 	[Title] Inductive Learning and Case-Based Reasoning [Abstract] This paper describes an application of an inductive learning techniques to case-based reasoning. We introduce two main forms of induction, define case-based reasoning and present a combination of both. The evaluation of the proposed system, called TA3, is carried out on a classification task, namely character recognition. We show how inductive knowledge improves knowledge representation and in turn flexibility of the system, its performance (in terms of classification accuracy) and its scalability. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #22876. 	[Title] A Case-Based Reasoning Approach [Abstract] The AAAI Fall Symposium; Flexible Computation in Intelligent Systems: Results, Issues, and Opportunities. Nov. 9-11, 1996, Cambridge, MA Abstract This paper presents a case-based reasoning system TA3. We address the flexibility of the case-based reasoning process, namely flexible retrieval of relevant experiences, by using a novel similarity assessment theory. To exemplify the advantages of such an approach, we have experimentally evaluated the system and compared its performance to the performance of non-flexible version of TA3 and to other machine learning algorithms on several domains. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #8703. 	[Title] Theory Refinement Combining Analytical and Empirical Methods [Abstract] This article describes a comprehensive approach to automatic theory revision. Given an imperfect theory, the approach combines explanation attempts for incorrectly classified examples in order to identify the failing portions of the theory. For each theory fault, correlated subsets of the examples are used to inductively generate a correction. Because the corrections are focused, they tend to preserve the structure of the original theory. Because the system starts with an approximate domain theory, in general fewer training examples are required to attain a given level of performance (classification accuracy) compared to a purely empirical system. The approach applies to classification systems employing a propositional Horn-clause theory. The system has been tested in a variety of application domains, and results are presented for problems in the domains of molecular biology and plant disease diagnosis. Please summarise all the given information.

Paper #22883 has the title `On the Informativeness of the DNA Promoter Sequences Domain Theory  
` and the abstract `The DNA promoter sequences domain theory and database have become popular for testing systems that integrate empirical and analytical learning. This note reports a simple change and reinterpretation of the domain theory in terms of M-of-N concepts, involving no learning, that results in an accuracy of 93.4% on the 106 items of the database. Moreover, an exhaustive search of the space of M-of-N domain theory interpretations indicates that the expected accuracy of a randomly chosen interpretation is 76.5%, and that a maximum accuracy of 97.2% is achieved in 12 cases. This demonstrates the informativeness of the domain theory, without the complications of understanding the interactions between various learning algorithms and the theory. In addition, our results help characterize the difficulty of learning using the DNA promoters theory.
`.
Question: Which category should Paper #22883 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #22883 has the title `On the Informativeness of the DNA Promoter Sequences Domain Theory  
` and the abstract `The DNA promoter sequences domain theory and database have become popular for testing systems that integrate empirical and analytical learning. This note reports a simple change and reinterpretation of the domain theory in terms of M-of-N concepts, involving no learning, that results in an accuracy of 93.4% on the 106 items of the database. Moreover, an exhaustive search of the space of M-of-N domain theory interpretations indicates that the expected accuracy of a randomly chosen interpretation is 76.5%, and that a maximum accuracy of 97.2% is achieved in 12 cases. This demonstrates the informativeness of the domain theory, without the complications of understanding the interactions between various learning algorithms and the theory. In addition, our results help characterize the difficulty of learning using the DNA promoters theory.
`.
Question: Which category should Paper #22883 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: Theory