Above are papers related to the following paper: [Paper_ID] Paper #1120786. 	[Title] Trees and Splines in Survival Analysis [Abstract] Technical Report No. 275 Revised March 30, 1995 University of Washington Department of Statistics Seattle, Washington 98195 Abstract During the past few years several nonparametric alternatives to the Cox proportional hazards model have appeared in the literature. These methods extend techniques that are well known from regression analysis to the analysis of censored survival data. In this paper we discuss methods based on (partition) trees and (polynomial) splines, analyze two datasets using both Survival Trees[1] and HARE[2], and compare the strengths and weaknesses of the two methods. One of the strengths of HARE is that its model fitting procedure has an implicit check for proportionality of the underlying hazards model. It also provides an explicit model for the conditional hazards function, which makes it very convenient to obtain graphical summaries. On the other hand, the tree-based methods automatically partition a dataset into groups of cases that are similar in survival history. Results obtained by survival trees and HARE are often complimentary. Trees and splines in survival analysis should provide the data analyst with two useful tools when analyzing survival data. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #151708. 	[Title] Using the Stochastic Gradient Method to Fit Polychotomous Regression Models [Abstract] Technical Report No. 319 April 7, 1997 University of Washington Department of Statistics Seattle, Washington 98195-4322 Abstract Kooperberg, Bose, and Stone (1997) introduced polyclass, a methodology that uses adaptively selected linear splines and their tensor products to model conditional class probabilities. The authors attempted to develop a methodology that would work well on small and moderate size problems and would scale up to large problems. However, the version of polyclass that was developed for large problems was impractical in that it required two months of cpu time to apply it to a large data set. A modification to this methodology involving the use of the stochastic gradient (on-line) method in fitting polyclass models to given sets of basis functions is developed here that makes the methodology applicable to large data sets. In particular, it is successfully applied to a phoneme recognition problem involving 45 phonemes, 81 features, 150,000 cases in the training sample, 1000 basis functions, and 44,000 unknown parameters. Comparisons with neural networks are made both on the original problem and on a three-vowel subproblem. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #4330. 	[Title] Hierarchical Mixtures of Experts and the EM Algorithm [Abstract] We present a tree-structured architecture for supervised learning. The statistical model underlying the architecture is a hierarchical mixture model in which both the mixture coefficients and the mixture components are generalized linear models (GLIM's). Learning is treated as a maximum likelihood problem; in particular, we present an Expectation-Maximization (EM) algorithm for adjusting the parameters of the architecture. We also develop an on-line learning algorithm in which the parameters are updated incrementally. Comparative simulation results are presented in the robot dynamics domain. This report describes research done at the Dept. of Brain and Cognitive Sciences, the Center for Biological and Computational Learning, and the Artificial Intelligence Laboratory of the Massachusetts Institute of Technology. Support for CBCL is provided in part by a grant from the NSF (ASC-9217041). Support for the laboratory's artificial intelligence research is provided in part by the Advanced Research Projects Agency of the Dept. of Defense. The authors were supported by a grant from the McDonnell-Pew Foundation, by a grant from ATR Human Information Processing Research Laboratories, by a grant from Siemens Corporation, by by grant IRI-9013991 from the National Science Foundation, by grant N00014-90-J-1942 from the Office of Naval Research, and by NSF grant ECS-9216531 to support an Initiative in Intelligent Control at MIT. Michael I. Jordan is a NSF Presidential Young Investigator. Please summarise all the given information.

Paper #1120786 has the title `Trees and Splines in Survival Analysis  
` and the abstract `Technical Report No. 275 Revised March 30, 1995 University of Washington Department of Statistics Seattle, Washington 98195 Abstract During the past few years several nonparametric alternatives to the Cox proportional hazards model have appeared in the literature. These methods extend techniques that are well known from regression analysis to the analysis of censored survival data. In this paper we discuss methods based on (partition) trees and (polynomial) splines, analyze two datasets using both Survival Trees[1] and HARE[2], and compare the strengths and weaknesses of the two methods. One of the strengths of HARE is that its model fitting procedure has an implicit check for proportionality of the underlying hazards model. It also provides an explicit model for the conditional hazards function, which makes it very convenient to obtain graphical summaries. On the other hand, the tree-based methods automatically partition a dataset into groups of cases that are similar in survival history. Results obtained by survival trees and HARE are often complimentary. Trees and splines in survival analysis should provide the data analyst with two useful tools when analyzing survival data.
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Question: Which category should Paper #1120786 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1120786 has the title `Trees and Splines in Survival Analysis  
` and the abstract `Technical Report No. 275 Revised March 30, 1995 University of Washington Department of Statistics Seattle, Washington 98195 Abstract During the past few years several nonparametric alternatives to the Cox proportional hazards model have appeared in the literature. These methods extend techniques that are well known from regression analysis to the analysis of censored survival data. In this paper we discuss methods based on (partition) trees and (polynomial) splines, analyze two datasets using both Survival Trees[1] and HARE[2], and compare the strengths and weaknesses of the two methods. One of the strengths of HARE is that its model fitting procedure has an implicit check for proportionality of the underlying hazards model. It also provides an explicit model for the conditional hazards function, which makes it very convenient to obtain graphical summaries. On the other hand, the tree-based methods automatically partition a dataset into groups of cases that are similar in survival history. Results obtained by survival trees and HARE are often complimentary. Trees and splines in survival analysis should provide the data analyst with two useful tools when analyzing survival data.
`.
Question: Which category should Paper #1120786 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