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 #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 #119761. 	[Title] Learning from Examples, Agent Teams and the Concept of Reflection [Abstract] In International Journal of Pattern Recognition and AI, 10(3):251-272, 1996 Also available as GMD report #766 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 #35061. 	[Title] Evolving Networks: Using the Genetic Algorithm with Connectionist Learning [Abstract]  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.

Above are papers related to the following paper: [Paper_ID] Paper #284023. 	[Title] Submitted to Circuits, Systems and Signal Processing Neural Network Constructive Algorithms: Trading Generalization for Learning Efficiency? [Abstract] There are currently several types of constructive, or growth, algorithms available for training a feed-forward neural network. This paper describes and explains the main ones, using a fundamental approach to the multi-layer perceptron problem-solving mechanisms. The claimed convergence properties of the algorithms are verified using just two mapping theorems, which consequently enables all the algorithms to be unified under a basic mechanism. The algorithms are compared and contrasted and the deficiencies of some highlighted. The fundamental reasons for the actual success of these algorithms are extracted, and used to suggest where they might most fruitfully be applied. A suspicion that they are not a panacea for all current neural network difficulties, and that one must somewhere along the line pay for the learning efficiency they promise, is developed into an argument that their generalization abilities will lie on average below that of back-propagation. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #93555. 	[Title] Multiple Network Systems (Minos) Modules: Task Division and Module Discrimination 1 [Abstract] It is widely considered an ultimate connectionist objective to incorporate neural networks into intelligent systems. These systems are intended to possess a varied repertoire of functions enabling adaptable interaction with a non-static environment. The first step in this direction is to develop various neural network algorithms and models, the second step is to combine such networks into a modular structure that might be incorporated into a workable system. In this paper we consider one aspect of the second point, namely: processing reliability and hiding of wetware details. Pre- sented is an architecture for a type of neural expert module, named an Authority. An Authority consists of a number of Minos modules. Each of the Minos modules in an Authority has the same processing capabilities, but varies with respect to its particular specialization to aspects of the problem domain. The Authority employs the collection of Minoses like a panel of experts. The expert with the highest confidence is believed, and it is the answer and confidence quotient that are transmitted to other levels in a system hierarchy. 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 #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 #1152958. 	[Title]  [Abstract] GMD Report #633 Abstract Many of the current artificial neural network systems have serious limitations, concerning accessibility, flexibility, scaling and reliability. In order to go some way to removing these we suggest a reflective neural network architecture. In such an architecture, the modular structure is the most important element. The building-block elements are called "minos' modules. They perform self-observation and inform on the current level of development, or scope of expertise, within the module. A Pandemonium system integrates such submodules so that they work together to handle mapping tasks. Network complexity limitations are attacked in this way with the Pandemonium problem decomposition paradigm, and both static and dynamic unreliability of the whole Pandemonium system is effectively eliminated through the generation and interpretation of confidence and ambiguity measures at every moment during the development of the system. Two problem domains are used to test and demonstrate various aspects of our architecture. Reliability and quality measures are defined for systems that only answer part of the time. Our system achieves better quality values than single networks of larger size for a handwritten digit problem. When both second and third best answers are accepted, our system is left with only 5% error on the test set, 2.1% better than the best single net. It is also shown how the system can elegantly learn to handle garbage patterns. With the parity problem it is demonstrated how complexity of problems may be decomposed automatically by the system, through solving it with networks of size smaller than a single net is required to be. Even when the system does not find a solution to the parity problem, because networks of too small a size are used, the reliability remains around 99-100%. Our Pandemonium architecture gives more power and flexibility to the higher levels of a large hybrid system than a single net system can, offering useful information for higher-level feedback loops, through which reliability of answers may be intelligently traded for less reliable but important "intuitional" answers. In providing weighted alternatives and possible generalizations, this architecture gives the best possible service to the larger system of which it will form part. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1152959. 	[Title] Rejection of Incorrect Answers from a Neural Net Classifier [Abstract] Frank Smieja Report number: 1993/2 Please summarise all the given information.

Paper #346292 has the title `Hyperplane Dynamics as a Means to Understanding Back-Propagation Learning and Network Plasticity  
` and the 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. 
`.
Question: Which category should Paper #346292 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #346292 has the title `Hyperplane Dynamics as a Means to Understanding Back-Propagation Learning and Network Plasticity  
` and the 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. 
`.
Question: Which category should Paper #346292 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