Above are papers related to the following paper: [Paper_ID] Paper #166825. 	[Title] A Preprocessing Model for Integrating CBR and Prototype-Based Neural Networks [Abstract] Some important factors that play a major role in determining the performances of a CBR (Case-Based Reasoning) system are the complexity and the accuracy of the retrieval phase. Both flat memory and inductive approaches suffer from serious drawbacks. In the first approach, the search time increases when dealing with large scale memory base, while in the second one the modification of the case memory becomes very complex because of its sophisticated architecture. In this paper, we show how we construct a simple efficient indexing system structure. The idea is to construct a case hierarchy with two levels of memory: the lower level contains cases organised into groups of similar cases, while the upper level contains prototypes. each prototype represents one group of cases. This smaller memory is used during the retrieval phase. Prototype construction is achieved by means of an incremental prototype-based NN (Neural Network). We show that this mode of CBR-NN coupling is a preprocessing one where the neural network serves as an indexing system to the Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1110024. 	[Title] Conceptual Analogy: Conceptual clustering for informed and efficient analogical reasoning [Abstract] Conceptual analogy (CA) is a general approach that applies conceptual clustering and concept representations to facilitate the efficient use of past experiences (cases) during analogical reasoning (Borner 1995). The approach was developed and implemented in SYN* (see also (Borner 1994, Borner and Faauer 1995)) to support the design of supply nets in building engineering. This paper sketches the task; it outlines the nearest-neighbor-based agglomerative conceptual clustering applied in organizing large amounts of structured cases into case classes; it provides the concept representation used to characterize case classes and shows the analogous solution of new problems based on the concepts available. However, the main purpose of this paper is to evaluate CA in terms of its reasoning efficiency; its capability to derive solutions that go beyond the cases in the case base but still preserve the quality of cases. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #15889. 	[Title] Structural Similarity as Guidance in Case-Based Design [Abstract] This paper presents a novel approach to determine structural similarity as guidance for adaptation in case-based reasoning (Cbr). We advance structural similarity assessment which provides not only a single numeric value but the most specific structure two cases have in common, inclusive of the modification rules needed to obtain this structure from the two cases. Our approach treats retrieval, matching and adaptation as a group of dependent processes. This guarantees the retrieval and matching of not only similar but adaptable cases. Both together enlarge the overall problem solving performance of Cbr and the explainability of case selection and adaptation considerably. Although our approach is more theoretical in nature and not restricted to a specific domain, we will give an example taken from the domain of industrial building design. Additionally, we will sketch two prototypical implementations of this approach. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1118388. 	[Title] Structural Similarity and Adaptation [Abstract] Most commonly, case-based reasoning is applied in domains where attribute value representations of cases are sufficient to represent the features relevant to support classification, diagnosis or design tasks. Distance functions like the Hamming-distance or their transformation into similarity functions are applied to retrieve past cases to be used to generate the solution of an actual problem. Often, domain knowledge is available to adapt past solutions to new problems or to evaluate solutions. However, there are domains like architectural design or law in which structural case representations and corresponding structural similarity functions are needed. Often, the acquisition of adaptation knowledge seems to be impossible or rather requires an effort that is not manageable for fielded applications. Despite of this, humans use cases as the main source to generate adapted solutions. How to achieve this computationally? This paper presents a general approach to structural similarity assessment and adaptation. The approach allows to explore structural case representations and limited domain knowledge to support design tasks. It is exemplarily instantiated in three modules of the design assistant FABEL-Idea that generates adapted design solutions on the basis of prior CAD layouts. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #15892. 	[Title] Automatic Indexing, Retrieval and Reuse of Topologies in Architectual Layouts [Abstract] Former layouts contain much of the know-how of architects. A generic and automatic way to formalize this know-how in order to use it by a computer would save a lot of effort and money. However, there seems to be no such way. The only access to the know-how are the layouts themselves. Developing a generic software tool to reuse former layouts you cannot consider every part of the architectual domain or things like personal style. Tools used today only consider small parts of the architectual domain. Any personal style is ignored. Isn't it possible to build a basic tool which is adjusted by the content of the former layouts, but may be extended incremently by modeling as much of the domain as desirable? This paper will describe a reuse tool to perform this task focusing on topological and geometrical binary relations. Please summarise all the given information.

Paper #166825 has the title `A Preprocessing Model for Integrating CBR and Prototype-Based Neural Networks  
` and the abstract `Some important factors that play a major role in determining the performances of a CBR (Case-Based Reasoning) system are the complexity and the accuracy of the retrieval phase. Both flat memory and inductive approaches suffer from serious drawbacks. In the first approach, the search time increases when dealing with large scale memory base, while in the second one the modification of the case memory becomes very complex because of its sophisticated architecture. In this paper, we show how we construct a simple efficient indexing system structure. The idea is to construct a case hierarchy with two levels of memory: the lower level contains cases organised into groups of similar cases, while the upper level contains prototypes. each prototype represents one group of cases. This smaller memory is used during the retrieval phase. Prototype construction is achieved by means of an incremental prototype-based NN (Neural Network). We show that this mode of CBR-NN coupling is a preprocessing one where the neural network serves as an indexing system to the 
`.
Question: Which category should Paper #166825 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #166825 has the title `A Preprocessing Model for Integrating CBR and Prototype-Based Neural Networks  
` and the abstract `Some important factors that play a major role in determining the performances of a CBR (Case-Based Reasoning) system are the complexity and the accuracy of the retrieval phase. Both flat memory and inductive approaches suffer from serious drawbacks. In the first approach, the search time increases when dealing with large scale memory base, while in the second one the modification of the case memory becomes very complex because of its sophisticated architecture. In this paper, we show how we construct a simple efficient indexing system structure. The idea is to construct a case hierarchy with two levels of memory: the lower level contains cases organised into groups of similar cases, while the upper level contains prototypes. each prototype represents one group of cases. This smaller memory is used during the retrieval phase. Prototype construction is achieved by means of an incremental prototype-based NN (Neural Network). We show that this mode of CBR-NN coupling is a preprocessing one where the neural network serves as an indexing system to the 
`.
Question: Which category should Paper #166825 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