Above are papers related to the following paper: [Paper_ID] Paper #643734. 	[Title] Data-defined Problems and Multiversion Neural-net Systems [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #643777. 	[Title] Self-Organizing Sets of Experts [Abstract] We describe and evaluate multi-network connectionist systems composed of "expert" networks. By preprocessing training data with a competitive learning network, the system automatically organizes the process of decomposition into expert subtasks. Using several different types of challenging problem, we assess this approach | the degree to which the automatically generated experts really are specialists on a predictable subset of the overall task, and a comparison of such decompositions with equivalent single-networks. In addition, we assess the utility of this approach alongside, and in competition to, non-expert multiversion systems. Previously developed measures of `diversity' for such systems are also applied to provide a quantitative assessment of the degree of specialization obtained in an expert-net ensemble. We show that on both types of problem, abstract well-defined and data-defined, the automatic decomposition does produce an effective set of specialist networks which together can support a high level of performance. Curiously, the study does not provide any support for a differential of effectiveness within the two classes of problem: continuous, homogeneous functions and discrete, discontinuous functions. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #643735. 	[Title] Use of Methodological Diversity to Improve Neural Network Generalisation [Abstract] Littlewood and Miller [1989] present a statistical framework for dealing with coincident failures in multiversion software systems. They develop a theoretical model that holds the promise of high system reliability through the use of multiple, diverse sets of alternative versions. In this paper we adapt their framework to investigate the feasibility of exploiting the diversity observable in multiple populations of neural networks developed using diverse methodologies. We evaluate the generalisation improvements achieved by a range of methodologically diverse network generation processes. We attempt to order the constituent methodological features with respect to their potential for use in the engineering of useful diversity. We also define and explore the use of relative measures of the diversity between version sets as a guide to the potential for exploiting inter-set diversity. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #32083. 	[Title] Back Propagation is Sensitive to Initial Conditions [Abstract] This paper explores the effect of initial weight selection on feed-forward networks learning simple functions with the back-propagation technique. We first demonstrate, through the use of Monte Carlo techniques, that the magnitude of the initial condition vector (in weight space) is a very significant parameter in convergence time variability. In order to further understand this result, additional deterministic experiments were performed. The results of these experiments demonstrate the extreme sensitivity of back propagation to initial weight configuration. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #200630. 	[Title] Replicability of Neural Computing Experiments [Abstract] If an experiment requires statistical analysis to establish a result, then one should do a better experiment. Ernest Rutherford, 1930 Most proponents of cold fusion reporting excess heat from their electrolysis experiments were claiming that one of the main characteristics of cold fusion was its irreproducibility | J.R. Huizenga, Cold Fusion, 1993, p. 78 Abstract Amid the ever increasing research into various aspects of neural computing, much progress is evident both from theoretical advances and from empirical studies. On the empirical side a wealth of data from experimental studies is being reported. It is, however, not clear how best to report neural computing experiments such that they may be replicated by other interested researchers. In particular, the nature of iterative learning on a randomised initial architecture, such as backpropagation training of a multilayer perceptron, is such that precise replication of a reported result is virtually impossible. The outcome is that experimental replication of reported results, a touchstone of "the scientific method", is not an option for researchers in this most popular subfield of neural computing. In this paper, we address this issue of replicability of experiments based on backpropagation training of multilayer perceptrons (although many of our results will be applicable to any other subfield that is plagued by the same characteristics). First, we attempt to produce a complete abstract specification of such a neural computing experiment. From this specification we identify the full range of parameters needed to support maximum replicability, and we use it to show why absolute replicability is not an option in practice. We propose a statistical framework to support replicability. We demonstrate this framework with some empirical studies of our own on both repli-cability with respect to experimental controls, and validity of implementations of the backpropagation algorithm. Finally, we suggest how the degree of replicability of a neural computing experiment can be estimated and reflected in the claimed precision for any empirical results reported. Please summarise all the given information.

Paper #643734 has the title `Data-defined Problems and Multiversion Neural-net Systems  
` and the abstract `nan`.
Question: Which category should Paper #643734 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #643734 has the title `Data-defined Problems and Multiversion Neural-net Systems  
` and the abstract `nan`.
Question: Which category should Paper #643734 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