Above are papers related to the following paper: [Paper_ID] Paper #27531. 	[Title] Using Sampling and Queries to Extract Rules from Trained Neural Networks [Abstract] Concepts learned by neural networks are difficult to understand because they are represented using large assemblages of real-valued parameters. One approach to understanding trained neural networks is to extract symbolic rules that describe their classification behavior. There are several existing rule-extraction approaches that operate by searching for such rules. We present a novel method that casts rule extraction not as a search problem, but instead as a learning problem. In addition to learning from training examples, our method exploits the property that networks can be efficiently queried. We describe algorithms for extracting both conjunctive and M -of-N rules, and present experiments that show that our method is more efficient than conventional search-based approaches. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1136449. 	[Title] Rearrangement of receptive field topography after intracortical and peripheral stimulation: The role of plasticity in [Abstract] Intracortical microstimulation (ICMS) of a single site in the somatosensory cortex of rats and monkeys for 2-6 hours produces a large increase in the number of neurons responsive to the skin region corresponding to the ICMS-site receptive field (RF), with very little effect on the position and size of the ICMS-site RF, and the response evoked at the ICMS site by tactile stimulation (Recanzone et al., 1992b). Large changes in RF topography are observed following several weeks of repetitive stimulation of a restricted skin region in monkeys (Jenkins et al., 1990; Recanzone et al., 1992acde). Repetitive stimulation of a localized skin region in monkeys produced by training the monkeys in a tactile frequency discrimination task improves their performance (Recanzone et al., 1992a). It has been suggested that these changes in RF topography are caused by competitive learning in excitatory pathways (Grajski & Merzenich, 1990; Jenkins et al., 1990; Recanzone et al., 1992abcde). ICMS almost simultaneously excites excitatory and inhibitory terminals and excitatory and inhibitory cortical neurons within a few microns of the stimulating electrode. Thus, this paper investigates the implications of the possibility that lateral inhibitory pathways too may undergo synaptic plasticity during ICMS. Lateral inhibitory pathways may also undergo synaptic plasticity in adult animals during peripheral conditioning. The "EXIN" (afferent excitatory and lateral inhibitory) synaptic plasticity rules Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #763009. 	[Title] Generalization and Exclusive Allocation of Credit in Unsupervised Category Learning [Abstract] Acknowledgements: This research was supported in part by the Office of Naval Research (Cognitive and Neural Sciences, N00014-93-1-0208) and by the Whitaker Foundation (Special Opportunity Grant). We thank George Kalarickal, Charles Schmitt, William Ross, and Douglas Kelly for valuable discussions. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1106370. 	[Title] The Effective Size of a Neural Network: A Principal Component Approach [Abstract] Often when learning from data, one attaches a penalty term to a standard error term in an attempt to prefer simple models and prevent overfitting. Current penalty terms for neural networks, however, often do not take into account weight interaction. This is a critical drawback since the effective number of parameters in a network usually differs dramatically from the total number of possible parameters. In this paper, we present a penalty term that uses Principal Component Analysis to help detect functional redundancy in a neural network. Results show that our new algorithm gives a much more accurate estimate of network complexity than do standard approaches. As a result, our new term should be able to improve techniques that make use of a penalty term, such as weight decay, weight pruning, feature selection, Bayesian, and prediction-risk tech niques. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #66594. 	[Title] Automatic Parameter Selection by Minimizing Estimated Error [Abstract] We address the problem of finding the parameter settings that will result in optimal performance of a given learning algorithm using a particular dataset as training data. We describe a "wrapper" method, considering determination of the best parameters as a discrete function optimization problem. The method uses best-first search and cross-validation to wrap around the basic induction algorithm: the search explores the space of parameter values, running the basic algorithm many times on training and holdout sets produced by cross-validation to get an estimate of the expected error of each parameter setting. Thus, the final selected parameter settings are tuned for the specific induction algorithm and dataset being studied. We report experiments with this method on 33 datasets selected from the UCI and StatLog collections using C4.5 as the basic induction algorithm. At a 90% confidence level, our method improves the performance of C4.5 on nine domains, degrades performance on one, and is statistically indistinguishable from C4.5 on the rest. On the sample of datasets used for comparison, our method yields an average 13% relative decrease in error rate. We expect to see similar performance improvements when using our method with other machine learning al gorithms. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #19621. 	[Title] References "Using Neural Networks to Identify Jets", Kohonen, "Self Organized Formation of Topologically Correct Feature [Abstract] 2] D. E. Rumelhart, G. E. Hinton and R. J. Williams, "Learning Internal Representations by Error Propagation", in D. E. Rumelhart and J. L. McClelland (eds.) Parallel Distributed Processing: Explorations in the Microstructure of Cognition (Vol. 1), MIT Press (1986). Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #24966. 	[Title] A Practical Bayesian Framework for Backprop Networks [Abstract] A quantitative and practical Bayesian framework is described for learning of mappings in feedforward networks. The framework makes possible: (1) objective comparisons between solutions using alternative network architectures; (2) objective stopping rules for network pruning or growing procedures; (3) objective choice of magnitude and type of weight decay terms or additive regularisers (for penalising large weights, etc.); (4) a measure of the effective number of well-determined parameters in a model; (5) quantified estimates of the error bars on network parameters and on network output; (6) objective comparisons with alternative learning and interpolation models such as splines and radial basis functions. The Bayesian `evidence' automatically embodies `Occam's razor,' penalising over-flexible and over-complex models. The Bayesian approach helps detect poor underlying assumptions in learning models. For learning models well matched to a problem, a good correlation between generalisation ability and the Bayesian evidence is obtained. This paper makes use of the Bayesian framework for regularisation and model comparison described in the companion paper `Bayesian interpolation' (MacKay, 1991a). This framework is due to Gull and Skilling (Gull, 1989a). Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1106630. 	[Title] Submitted to the Future Generation Computer Systems special issue on Data Mining. Using Neural Networks [Abstract] Neural networks have been successfully applied in a wide range of supervised and unsupervised learning applications. Neural-network methods are not commonly used for data-mining tasks, however, because they often produce incomprehensible models and require long training times. In this article, we describe neural-network learning algorithms that are able to produce comprehensible models, and that do not require excessive training times. Specifically, we discuss two classes of approaches for data mining with neural networks. The first type of approach, often called rule extraction, involves extracting symbolic models from trained neural networks. The second approach is to directly learn simple, easy-to-understand networks. We argue that, given the current state of the art, neural-network methods deserve a place in the tool boxes of data-mining specialists. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #2665. 	[Title] An information-maximisation approach to blind separation and blind deconvolution [Abstract] We derive a new self-organising learning algorithm which maximises the information transferred in a network of non-linear units. The algorithm does not assume any knowledge of the input distributions, and is defined here for the zero-noise limit. Under these conditions, information maximisation has extra properties not found in the linear case (Linsker 1989). The non-linearities in the transfer function are able to pick up higher-order moments of the input distributions and perform something akin to true redundancy reduction between units in the output representation. This enables the network to separate statistically independent components in the inputs: a higher-order generalisation of Principal Components Analysis. We apply the network to the source separation (or cocktail party) problem, successfully separating unknown mixtures of up to ten speakers. We also show that a variant on the network architecture is able to perform blind deconvolution (cancellation of unknown echoes and reverberation in a speech signal). Finally, we derive dependencies of information transfer on time delays. We suggest that information max-imisation provides a unifying framework for problems in `blind' signal processing. fl Please send comments to tony@salk.edu. This paper will appear as Neural Computation, 7, 6, 1004-1034 (1995). The reference for this version is: Technical Report no. INC-9501, February 1995, Institute for Neural Computation, UCSD, San Diego, CA 92093-0523. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #27530. 	[Title] Learning Symbolic Rules Using Artificial Neural Networks [Abstract] A distinct advantage of symbolic learning algorithms over artificial neural networks is that typically the concept representations they form are more easily understood by humans. One approach to understanding the representations formed by neural networks is to extract symbolic rules from trained networks. In this paper we describe and investigate an approach for extracting rules from networks that uses (1) the NofM extraction algorithm, and (2) the network training method of soft weight-sharing. Previously, the NofM algorithm had been successfully applied only to knowledge-based neural networks. Our experiments demonstrate that our extracted rules generalize better than rules learned using the C4.5 system. In addition to being accurate, our extracted rules are also reasonably comprehensible. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1136446. 	[Title] The role of afferent excitatory and lateral inhibitory synaptic plasticity in visual cortical ocular dominance [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1135125. 	[Title] Constructing Fuzzy Graphs from Examples [Abstract] Methods to build function approximators from example data have gained considerable interest in the past. Especially methodologies that build models that allow an interpretation have attracted attention. Most existing algorithms, however, are either complicated to use or infeasible for high-dimensional problems. This article presents an efficient and easy to use algorithm to construct fuzzy graphs from example data. The resulting fuzzy graphs are based on locally independent fuzzy rules that operate solely on selected, important attributes. This enables the application of these fuzzy graphs also to problems in high dimensional spaces. Using illustrative examples and a real world data set it is demonstrated how the resulting fuzzy graphs offer quick insights into the structure of the example data, that is, the underlying model. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1136447. 	[Title] Plasticity in cortical neuron properties: Modeling the effects of an NMDA antagonist and a GABA [Abstract] Infusion of a GABA agonist (Reiter & Stryker, 1988) and infusion of an NMDA receptor antagonist (Bear et al., 1990), in the primary visual cortex of kittens during monocular deprivation, shifts ocular dominance toward the closed eye, in the cortical region near the infusion site. This reverse ocular dominance shift has been previously modeled by variants of a covariance synaptic plasticity rule (Bear et al., 1990; Clothiaux et al., 1991; Miller et al., 1989; Reiter & Stryker, 1988). Kasamatsu et al. (1997, 1998) showed that infusion of an NMDA receptor antagonist in adult cat primary visual cortex changes ocular dominance distribution, reduces binocularity, and reduces orientation and direction selectivity. This paper presents a novel account of the effects of these pharmacological treatments, based on the EXIN synaptic plasticity rules (Marshall, 1995), which include both an instar afferent excitatory and an outstar lateral inhibitory rule. Functionally, the EXIN plasticity rules enhance the efficiency, discrimination, and context-sensitivity of a neural network's representation of perceptual patterns (Marshall, 1995; Marshall & Gupta, 1998). The EXIN model decreases lateral inhibition from neurons outside the infusion site (control regions) to neurons inside the infusion region, during monocular deprivation. In the model, plasticity in afferent pathways to neurons affected by the pharmacological treatments is assumed to be blocked , as opposed to previous models (Bear et al., 1990; Miller et al., 1989; Reiter & Stryker, 1988), in which afferent pathways from the open eye to neurons in the infusion region are weakened . The proposed model is consistent with results suggesting that long-term plasticity can be blocked by NMDA antagonists or by postsynaptic hyperpolarization (Bear et al., 1990; Dudek & Bear, 1992; Goda & Stevens, 1996; Kirkwood et al., 1993). Since the role of plasticity in lateral inhibitory pathways in producing cortical plasticity has not received much attention, several predictions are made based on the EXIN lateral inhibitory plasticity rule. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #27543. 	[Title] Extracting Provably Correct Rules from Artificial Neural Networks [Abstract] Although connectionist learning procedures have been applied successfully to a variety of real-world scenarios, artificial neural networks have often been criticized for exhibiting a low degree of comprehensibility. Mechanisms that automatically compile neural networks into symbolic rules offer a promising perspective to overcome this practical shortcoming of neural network representations. This paper describes an approach to neural network rule extraction based on Validity Interval Analysis (VI-Analysis). VI-Analysis is a generic tool for extracting symbolic knowledge from Backpropagation-style artificial neural networks. It does this by propagating whole intervals of activations through the network in both the forward and backward directions. In the context of rule extraction, these intervals are used to prove or disprove the correctness of conjectured rules. We describe techniques for generating and testing rule hypotheses, and demonstrate these using some simple classification tasks including the MONK's benchmark problems. Rules extracted by VI-Analysis are provably correct. No assumptions are made about the topology of the network at hand, as well as the procedure employed for training the network. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #592826. 	[Title] Machine learning in prognosis of the femoral neck fracture recovery examples, estimating attributes, explanation ability, [Abstract] We compare the performance of several machine learning algorithms in the problem of prognos-tics of the femoral neck fracture recovery: the K-nearest neighbours algorithm, the semi-naive Bayesian classifier, backpropagation with weight elimination learning of the multilayered neural networks, the LFC (lookahead feature construction) algorithm, and the Assistant-I and Assistant-R algorithms for top down induction of decision trees using information gain and RELIEFF as search heuristics, respectively. We compare the prognostic accuracy and the explanation ability of different classifiers. Among the different algorithms the semi-naive Bayesian classifier and Assistant-R seem to be the most appropriate. We analyze the combination of decisions of several classifiers for solving prediction problems and show that the combined classifier improves both performance and the explanation ability. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1128542. 	[Title] Knowledge Integration and Rule Extraction in Neural Networks Ph.D. Proposal [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #3231. 	[Title] Irrelevant Features and the Subset Selection Problem [Abstract] We address the problem of finding a subset of features that allows a supervised induction algorithm to induce small high-accuracy concepts. We examine notions of relevance and irrelevance, and show that the definitions used in the machine learning literature do not adequately partition the features into useful categories of relevance. We present definitions for irrelevance and for two degrees of relevance. These definitions improve our understanding of the behavior of previous subset selection algorithms, and help define the subset of features that should be sought. The features selected should depend not only on the features and the target concept, but also on the induction algorithm. We describe a method for feature subset selection using cross-validation that is applicable to any induction algorithm, and discuss experiments conducted with ID3 and C4.5 on artificial and real datasets. Please summarise all the given information.

Paper #27531 has the title `Using Sampling and Queries to Extract Rules from Trained Neural Networks  
` and the abstract `Concepts learned by neural networks are difficult to understand because they are represented using large assemblages of real-valued parameters. One approach to understanding trained neural networks is to extract symbolic rules that describe their classification behavior. There are several existing rule-extraction approaches that operate by searching for such rules. We present a novel method that casts rule extraction not as a search problem, but instead as a learning problem. In addition to learning from training examples, our method exploits the property that networks can be efficiently queried. We describe algorithms for extracting both conjunctive and M -of-N rules, and present experiments that show that our method is more efficient than conventional search-based approaches.
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Question: Which category should Paper #27531 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #27531 has the title `Using Sampling and Queries to Extract Rules from Trained Neural Networks  
` and the abstract `Concepts learned by neural networks are difficult to understand because they are represented using large assemblages of real-valued parameters. One approach to understanding trained neural networks is to extract symbolic rules that describe their classification behavior. There are several existing rule-extraction approaches that operate by searching for such rules. We present a novel method that casts rule extraction not as a search problem, but instead as a learning problem. In addition to learning from training examples, our method exploits the property that networks can be efficiently queried. We describe algorithms for extracting both conjunctive and M -of-N rules, and present experiments that show that our method is more efficient than conventional search-based approaches.
`.
Question: Which category should Paper #27531 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