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.

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 #1120962. 	[Title] Experiments on the Transfer of Knowledge between Neural Networks Reprinted from: Computational Learning Theory and [Abstract] This chapter describes three studies which address the question of how neural network learning can be improved via the incorporation of information extracted from other networks. This general problem, which we call network transfer, encompasses many types of relationships between source and target networks. Our focus is on the utilization of weights from source networks which solve a subproblem of the target network task, with the goal of speeding up learning on the target task. We demonstrate how the approach described here can improve learning speed by up to ten times over learning starting with random weights. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #45605. 	[Title] Learning and evolution in neural networks [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #688361. 	[Title] Neural Network Implementation in SAS R Software [Abstract] The estimation or training methods in the neural network literature are usually some simple form of gradient descent algorithm suitable for implementation in hardware using massively parallel computations. For ordinary computers that are not massively parallel, optimization algorithms such as those in several SAS procedures are usually far more efficient. This talk shows how to fit neural networks using SAS/OR R fl , SAS/ETS R fl , and SAS/STAT R fl software. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1113995. 	[Title] Multiassociative Memory [Abstract] This paper discusses the problem of how to implement many-to-many, or multi-associative, mappings within connectionist models. Traditional symbolic approaches wield explicit representation of all alternatives via stored links, or implicitly through enumerative algorithms. Classical pattern association models ignore the issue of generating multiple outputs for a single input pattern, and while recent research on recurrent networks is promising, the field has not clearly focused upon multi-associativity as a goal. In this paper, we define multiassociative memory MM, and several possible variants, and discuss its utility in general cognitive modeling. We extend sequential cascaded networks (Pollack 1987, 1990a) to fit the task, and perform several ini tial experiments which demonstrate the feasibility of the concept. This paper appears in The Proceedings of the Thirteenth Annual Conference of the Cognitive Science Society. August 7-10, 1991. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1153933. 	[Title] Scaling-up RAAMs [Abstract] Modifications to Recursive Auto-Associative Memory are presented, which allow it to store deeper and more complex data structures than previously reported. These modifications include adding extra layers to the compressor and reconstructor networks, employing integer rather than real-valued representations, pre-conditioning the weights and pre-setting the representations to be compatible with them. The resulting system is tested on a data set of syntactic trees extracted from the Penn Treebank. 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 #6771. 	[Title] Statistical Tests for Comparing Supervised Classification Learning Algorithms [Abstract] This paper reviews five statistical tests for determining whether one learning algorithm outperforms another on a particular learning task. These tests are compared experimentally to determine their probability of incorrectly detecting a difference when no difference exists (type 1 error). Two widely-used statistical tests are shown to have high probability of Type I error in certain situations and should never be used. These tests are (a) a test for the difference of two proportions and (b) a paired-differences t test based on taking several random train/test splits. A third test, a paired-differences t test based on 10-fold cross-validation, exhibits somewhat elevated probability of Type I error. A fourth test, McNemar's test, is shown to have low Type I error. The fifth test is a new test, 5x2cv, based on 5 iterations of 2-fold cross-validation. Experiments show that this test also has good Type I error. The paper also measures the power (ability to detect algorithm differences when they do exist) of these tests. The 5x2cv test is shown to be slightly more powerful than McNemar's test. The choice of the best test is determined by the computational cost of running the learning algorithm. For algorithms that can be executed only once, McNemar's test is the only test with acceptable Type I error. For algorithms that can be executed ten times, the 5x2cv test is recommended, because it is slightly more powerful and because it directly measures variation due to the choice of training set. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #346292. 	[Title] Hyperplane Dynamics as a Means to Understanding Back-Propagation Learning and Network Plasticity [Abstract] The processing performed by a feed-forward neural network is often interpreted through use of decision hyperplanes at each layer. The adaptation process, however, is normally explained using the picture of gradient descent of an error landscape. In this paper the dynamics of the decision hyperplanes is used as the model of the adaptation process. A electro-mechanical analogy is drawn where the dynamics of hyperplanes is determined by interaction forces between hyperplanes and the particles which represent the patterns. Relaxation of the system is determined by increasing hyperplane inertia (mass). This picture is used to clarify the dynamics of learning, and to go some way to explaining learning deadlocks and escaping from certain local minima. Furthermore network plasticity is introduced as a dynamic property of the system, and its reduction as a necessary consequence of information storage. Hyper-plane inertia is used to explain and avoid destructive relearning in trained networks. Please summarise all the given information.

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 #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 #31769. 	[Title] Representing and Learning Visual Schemas in Neural Networks for Scene Analysis [Abstract] Using scene analysis as the task, this research focuses on three fundamental problems in neural network systems: (1) limited processing resources, (2) representing schemas, and (3) learning schemas. The first problem arises because no practical neural network can process all the visual input simultaneously and efficiently. The solution is to process a small amount of the input in parallel, and successively focus on the other parts of the input. This strategy requires that the system maintains structured knowledge for describing and interpreting the gathered information. The system should also learn to represent structured knowledge from examples of objects and scenes. VISOR, the system described in this paper, consists of three main components. The Low-Level Visual Module (simulated using procedural programs) extracts featural and positional information from the visual input. The Schema Module encodes structured knowledge about possible objects, and provides top-down information for the Low-Level Visual Module to focus attention at different parts of the scene. The Response Module learns to associate the schema activation patterns with external responses. It enables the external environment to provide reinforcement feedback for the learning of schematic structures. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #35061. 	[Title] Evolving Networks: Using the Genetic Algorithm with Connectionist Learning [Abstract]  Please summarise all the given information.

Paper #200630 has the title `Replicability of Neural Computing Experiments  
` and the 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. 
`.
Question: Which category should Paper #200630 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #200630 has the title `Replicability of Neural Computing Experiments  
` and the 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. 
`.
Question: Which category should Paper #200630 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