Above are papers related to the following paper: [Paper_ID] Paper #50838. 	[Title] Clustering Learning Tasks and the Selective Cross-Task Transfer of Knowledge [Abstract] This research is sponsored in part by the National Science Foundation under award IRI-9313367, and by the Wright Laboratory, Aeronautical Systems Center, Air Force Materiel Command, USAF, and the Advanced Research Projects Agency (ARPA) under grant number F33615-93-1-1330. The views and conclusions contained in this document are those of the author and should not be interpreted as necessarily representing official policies or endorsements, either expressed or implied, of NSF, Wright Laboratory or the United States Government. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #25794. 	[Title] Is Learning The n-th Thing Any Easier Than Learning The First? [Abstract] This paper investigates learning in a lifelong context. Lifelong learning addresses situations in which a learner faces a whole stream of learning tasks. Such scenarios provide the opportunity to transfer knowledge across multiple learning tasks, in order to generalize more accurately from less training data. In this paper, several different approaches to lifelong learning are described, and applied in an object recognition domain. It is shown that across the board, lifelong learning approaches generalize consistently more accurately from less training data, by their ability to transfer knowledge across learning tasks. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #582343. 	[Title] Transferring and Retraining Learned Information Filters [Abstract] Any system that learns how to filter documents will suffer poor performance during an initial training phase. One way of addressing this problem is to exploit filters learned by other users in a collaborative fashion. We investigate "direct transfer" of learned filters in this setting|a limiting case for any collaborative learning system. We evaluate the stability of several different learning methods under direct transfer, and conclude that symbolic learning methods that use negatively correlated features of the data perform poorly in transfer, even when they perform well in more conventional evaluation settings. This effect is robust: it holds for several learning methods, when a diverse set of users is used in training the classifier, and even when the learned classifiers can be adapted to the new user's distribution. Our experiments give rise to several concrete proposals for improving generalization performance in a collaborative setting, including a beneficial variation on a feature selection method that has been widely used in text categorization. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1126503. 	[Title] LEARNING MORE FROM LESS DATA: EXPERIMENTS WITH LIFELONG ROBOT LEARNING [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #25772. 	[Title] Flexible Metric Nearest Neighbor Classiflcation [Abstract] The K-nearest-neighbor decision rule assigns an object of unknown class to the plurality class among the K labeled "training" objects that are closest to it. Closeness is usually deflned in terms of a metric distance on the Euclidean space with the input measurement variables as axes. The metric chosen to deflne this distance can strongly efiect performance. An optimal choice depends on the problem at hand as characterized by the respective class distributions on the input measurement space, and within a given problem, on the location of the unknown object in that space. In this paper new types of K-nearest-neighbor procedures are described that estimate the local relevance of each input variable, or their linear combinations, for each individual point to be classifled. This information is then used to separately customize the metric used to deflne distance from that object in flnding its nearest neighbors. These procedures are a hybrid between regular K-nearest-neighbor methods and treestructured recursive partitioning techniques popular in statistics and machine learning. Please summarise all the given information.

Paper #50838 has the title `Clustering Learning Tasks and the Selective Cross-Task Transfer of Knowledge  
` and the abstract `This research is sponsored in part by the National Science Foundation under award IRI-9313367, and by the Wright Laboratory, Aeronautical Systems Center, Air Force Materiel Command, USAF, and the Advanced Research Projects Agency (ARPA) under grant number F33615-93-1-1330. The views and conclusions contained in this document are those of the author and should not be interpreted as necessarily representing official policies or endorsements, either expressed or implied, of NSF, Wright Laboratory or the United States Government. 
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Question: Which category should Paper #50838 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #50838 has the title `Clustering Learning Tasks and the Selective Cross-Task Transfer of Knowledge  
` and the abstract `This research is sponsored in part by the National Science Foundation under award IRI-9313367, and by the Wright Laboratory, Aeronautical Systems Center, Air Force Materiel Command, USAF, and the Advanced Research Projects Agency (ARPA) under grant number F33615-93-1-1330. The views and conclusions contained in this document are those of the author and should not be interpreted as necessarily representing official policies or endorsements, either expressed or implied, of NSF, Wright Laboratory or the United States Government. 
`.
Question: Which category should Paper #50838 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: Neural_Networks