Above are papers related to the following paper: [Paper_ID] Paper #1121176. 	[Title] Utilizing Connectionist Learning Procedures in Symbolic Case Retrieval Nets [Abstract] This paper describes a method which, under certain circumstances, allows to automatically learn or adjust similarity measures. For this, ideas of connectionist learning procedures, in particular those related to Hebbian learning, are combined with a Case-Based Reasoning engine. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #5062. 	[Title] A Memory Model for Case Retrieval by Activation Passing [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #5064. 	[Title] Case Retrieval Nets: Basic Ideas and Extensions [Abstract] An efficient retrieval of a relatively small number of relevant cases from a huge case base is a crucial subtask of Case-Based Reasoning. In this article, we present Case Retrieval Nets (CRNs), a memory model that has recently been developed for this task. The main idea is to apply a spreading activation process to a net-like case memory in order to retrieve cases being similar to a posed query case. We summarize the basic ideas of CRNs, suggest some useful extensions, and present some initial experimental results which suggest that CRNs can successfully handle case bases larger than considered usually in the CBR community. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #385067. 	[Title] CBR for Document Retrieval: The FAllQ Project [Abstract] This paper reports about a project on document retrieval in an industrial setting. The objective is to provide a tool that helps finding documents related to a given query, such as answers in Frequently Asked Questions databases. A CBR approach has been used to develop a running prototypical system which is currently under practical evaluation. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #5069. 	[Title] Applying Case Retrieval Nets to Diagnostic Tasks in Technical Domains [Abstract] This paper presents Objectdirected Case Retrieval Nets, a memory model developed for an application of Case-Based Reasoning to the task of technical diagnosis. The key idea is to store cases, i.e. observed symptoms and diagnoses, in a network and to enhance this network with an object model encoding knowledge about the devices in the application domain. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1129106. 	[Title] Technical Diagnosis: Fallexperte-D of further knowledge sources (domain knowledge, common knowledge) is investigated in the [Abstract] Case based reasoning (CBR) uses the knowledge from former experiences ("known cases"). Since special knowledge of an expert is mainly subject to his experiences, the CBR techniques are a good base for the development of expert systems. We investigate the problem for technical diagnosis. Diagnosis is not considered as a classification task, but as a process to be guided by computer assisted experience. This corresponds to the flexible "case completion" approach. Flexibility is also needed for the expert view with predominant interest in the unexpected, unpredictible cases. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #5075. 	[Title] An Investigation of Marker-Passing Algorithms for Analogue Retrieval [Abstract] If analogy and case-based reasoning systems are to scale up to very large case bases, it is important to analyze the various methods used for retrieving analogues to identify the features of the problem for which they are appropriate. This paper reports on one such analysis, a comparison of retrieval by marker passing or spreading activation in a semantic network with Knowledge-Directed Spreading Activation, a method developed to be well-suited for retrieving semantically distant analogues from a large knowledge base. The analysis has two complementary components: (1) a theoretical model of the retrieval time based on a number of problem characteristics, and (2) experiments showing how the retrieval time of the approaches varies with the knowledge base size. These two components, taken together, suggest that KDSA is more likely than SA to be able to scale up to retrieval in large knowledge bases. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1120563. 	[Title] Preparing Case Retrieval Nets for Distributed Processing [Abstract] In this paper, we discuss two approaches of applying the memory model of Case Retrieval Nets to applications where a distributed processing of information is required. For this, we distinguish two types of such applications, namely (a) the case of distributed case libraries and (b) the case of distributed cases. While a solution to the former is straightforward, the latter requires an extension to Case Retrieval Nets which provides a kind of partitioning of the entire net structure. This extended model even allows for a concurrent implementation of the retrieval process or for the use of collaborative agents for retrieval. Keywords: Case-based reasoning, case retrieval, memory structures, distributed processing. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #272345. 	[Title] CBR on Semi-structured Documents: The ExperienceBook and the FAllQ Project [Abstract] In this article, we present a case-based approach on flexible query answering systems in two different application areas: The ExperienceBook supports technical diagnosis in the field of system administration. In the FAllQ project we use our CBR system for document retrieval in an industrial setting. The objective of these systems is to manage knowledge stored in less structured documents. The internal case memory is implemented as a Case Retrieval Net. This allows to handle large case bases with an efficient retrieval process. In order to provide multi user access we chose the client server model combined with a web interface. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #28026. 	[Title] Case Retrieval Nets Applied to Large Case Bases [Abstract] This article presents some experimental results obtained from applying the Case Retrieval Net approach to large case bases. The obtained results suggest that CRNs can successfully handle case bases larger than considered in other reports. Please summarise all the given information.

Paper #1121176 has the title `Utilizing Connectionist Learning Procedures in Symbolic Case Retrieval Nets  
` and the abstract `This paper describes a method which, under certain circumstances, allows to automatically learn or adjust similarity measures. For this, ideas of connectionist learning procedures, in particular those related to Hebbian learning, are combined with a Case-Based Reasoning engine. 
`.
Question: Which category should Paper #1121176 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1121176 has the title `Utilizing Connectionist Learning Procedures in Symbolic Case Retrieval Nets  
` and the abstract `This paper describes a method which, under certain circumstances, allows to automatically learn or adjust similarity measures. For this, ideas of connectionist learning procedures, in particular those related to Hebbian learning, are combined with a Case-Based Reasoning engine. 
`.
Question: Which category should Paper #1121176 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