Above are papers related to the following paper: [Paper_ID] Paper #87482. 	[Title] A Multi-Chip Module Implementation of a Neural Network [Abstract] The requirement for dense interconnect in artificial neural network systems has led researchers to seek high-density interconnect technologies. This paper reports an implementation using multi-chip modules (MCMs) as the interconnect medium. The specific system described is a self-organizing, parallel, and dynamic learning model which requires a dense interconnect technology for effective implementation; this requirement is fulfilled by exploiting MCM technology. The ideas presented in this paper regarding an MCM implementation of artificial neural networks are versatile and can be adapted to apply to other neural network and connectionist models. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1111186. 	[Title] Using Multiple Node Types to Improve the Performance of DMP (Dynamic Multilayer Perceptron) [Abstract] This paper discusses a method for training multilayer perceptron networks called DMP2 (Dynamic Multilayer Perceptron 2). The method is based upon a divide and conquer approach which builds networks in the form of binary trees, dynamically allocating nodes and layers as needed. The focus of this paper is on the effects of using multiple node types within the DMP framework. Simulation results show that DMP2 performs favorably in comparison with other learning algorithms, and that using multiple node types can be beneficial to network performance. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1114398. 	[Title] The Effect of Decision Surface Fitness on Dynamic Multilayer Perceptron Networks (DMP1) [Abstract] The DMP1 (Dynamic Multilayer Perceptron 1) network training method is based upon a divide and conquer approach which builds networks in the form of binary trees, dynamically allocating nodes and layers as needed. This paper introduces the DMP1 method, and compares the preformance of DMP1 when using the standard delta rule training method for training individual nodes against the performance of DMP1 when using a genetic algorithm for training. While the basic model does not require the use of a genetic algorithm for training individual nodes, the results show that the convergence properties of DMP1 are enhanced by the use of a genetic algorithm with an appropriate fitness function. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #90655. 	[Title] Word Perfect Corp. A TRANSFORMATION FOR IMPLEMENTING NEURAL NETWORKS WITH LOCALIST PROPERTIES [Abstract] Most Artificial Neural Networks (ANNs) have a fixed topology during learning, and typically suffer from a number of shortcomings as a result. Variations of ANNs that use dynamic topologies have shown ability to overcome many of these problems. This paper introduces Location-Independent Transformations (LITs) as a general strategy for implementing feedforward networks that use dynamic topologies. A LIT creates a set of location-independent nodes, where each node computes its part of the network output independent of other nodes, using local information. This type of transformation allows efficient support for adding and deleting nodes dynamically during learning. In particular, this paper presents LITs for the single-layer competitve learning network, and the counterpropagation network, which combines elements of supervised learning with competitive learning. These two networks are localist in the sense that ultimately one node is responsible for each output. LITs for other models are presented in other papers. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1116974. 	[Title] Towards a General Distributed Platform for Learning and Generalization and Word Perfect Corp. 1 Introduction [Abstract] Different learning models employ different styles of generalization on novel inputs. This paper proposes the need for multiple styles of generalization to support a broad application base. The Priority ASOCS model (Priority Adaptive Self-Organizing Concurrent System) is overviewed and presented as a potential platform which can support multiple generalization styles. PASOCS is an adaptive network composed of many simple computing elements operating asynchronously and in parallel. The PASOCS can operate in either a data processing mode or a learning mode. During data processing mode, the system acts as a parallel hardware circuit. During learning mode, the PASOCS incorporates rules, with attached priorities, which represent the application being learned. Learning is accomplished in a distributed fashion in time logarithmic in the number of rules. The new model has significant learning time and space complexity improvements over previous models. Generalization in a learning system is at best always a guess. The proper style of generalization is application dependent. Thus, one style of generalization may not be sufficient to allow a learning system to support a broad spectrum of applications [14]. Current connectionist models use one specific style of generalization which is implicit in the learning algorithm. We suggest that the type of generalization used be a self-organizing parameter of the learning system which can be discovered as learning takes place. This requires a) a model which allows flexible generalization styles, and b) mechanisms to guide the system into the best style of generalization for the problem being learned. This paper overviews a learning model which seeks to efficiently support requirement a) above. The model is called Priority ASOCS (PASOCS) [9], which is a member of a class of models called ASOCS (Adaptive Self-Organizing Concurrent Systems) [5]. Section 2 of this paper gives an example of how different generalization techniques can approach a problem. Section 3 presents an overview of PASOCS. Section 4 illustrates how flexible generalization can be supported. Section 5 concludes the paper. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #74427. 	[Title] A Self-Organizing Binary Decision Tree For Incrementally Defined Rule Based [Abstract] This paper presents an ASOCS (adaptive self-organizing concurrent system) model for massively parallel processing of incrementally defined rule systems in such areas as adaptive logic, robotics, logical inference, and dynamic control. An ASOCS is an adaptive network composed of many simple computing elements operating asynchronously and in parallel. This paper focuses on adaptive algorithm 3 (AA3) and details its architecture and learning algorithm. It has advantages over previous ASOCS models in simplicity, implementability, and cost. An ASOCS can operate in either a data processing mode or a learning mode. During the data processing mode, an ASOCS acts as a parallel hardware circuit. In learning mode, rules expressed as boolean conjunctions are incrementally presented to the ASOCS. All ASOCS learning algorithms incorporate a new rule in a distributed fashion in a short, bounded time. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1115456. 	[Title] Automatic Feature Extraction in Machine Learning [Abstract] This thesis presents a machine learning model capable of extracting discrete classes out of continuous valued input features. This is done using a neurally inspired novel competitive classifier (CC) which feeds the discrete classifications forward to a supervised machine learning model. The supervised learning model uses the discrete classifications and perhaps other information available to solve a problem. The supervised learner then generates feedback to guide the CC into potentially more useful classifications of the continuous valued input features. Two supervised learning models are combined with the CC creating ASOCS-AFE and ID3-AFE. Both models are simulated and the results are analyzed. Based on these results, several areas of future research are proposed. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #340299. 	[Title] Neural Network Applicability: Classifying the Problem Space [Abstract] The tremendous current effort to propose neurally inspired methods of computation forces closer scrutiny of real world application potential of these models. This paper categorizes applications into classes and particularly discusses features of applications which make them efficiently amenable to neural network methods. Computational machines do deterministic mappings of inputs to outputs and many computational mechanisms have been proposed for problem solutions. Neural network features include parallel execution, adaptive learning, generalization, and fault tolerance. Often, much effort is given to a model and applications which can already be implemented in a much more efficient way with an alternate technology. Neural networks are potentially powerful devices for many classes of applications, but not all. However, it is proposed that the class of applications for which neural networks are efficient is both large and commonly occurring in nature. Comparison of supervised, unsupervised, and generalizing systems is also included. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #34257. 	[Title] A Self-Adjusting Dynamic Logic Module [Abstract] This paper presents an ASOCS (Adaptive Self-Organizing Concurrent System) model for massively parallel processing of incrementally defined rule systems in such areas as adaptive logic, robotics, logical inference, and dynamic control. An ASOCS is an adaptive network composed of many simple computing elements operating asynchronously and in parallel. This paper focuses on Adaptive Algorithm 2 (AA2) and details its architecture and learning algorithm. AA2 has significant memory and knowledge maintenance advantages over previous ASOCS models. An ASOCS can operate in either a data processing mode or a learning mode. During learning mode, the ASOCS is given a new rule expressed as a boolean conjunction. The AA2 learning algorithm incorporates the new rule in a distributed fashion in a short, bounded time. During data processing mode, the ASOCS acts as a parallel hardware circuit. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1122642. 	[Title] Word Perfect Corp. LIA: A Location-Independent Transformation for ASOCS Adaptive Algorithm 2 [Abstract] Most Artificial Neural Networks (ANNs) have a fixed topology during learning, and often suffer from a number of shortcomings as a result. ANNs that use dynamic topologies have shown ability to overcome many of these problems. Adaptive Self Organizing Concurrent Systems (ASOCS) are a class of learning models with inherently dynamic topologies. This paper introduces Location-Independent Transformations (LITs) as a general strategy for implementing learning models that use dynamic topologies efficiently in parallel hardware. A LIT creates a set of location-independent nodes, where each node computes its part of the network output independent of other nodes, using local information. This type of transformation allows efficient support for adding and deleting nodes dynamically during learning. In particular, this paper presents the Location - Independent ASOCS (LIA) model as a LIT for ASOCS Adaptive Algorithm 2. The description of LIA gives formal definitions for LIA algorithms. Because LIA implements basic ASOCS mechanisms, these definitions provide a formal description of basic ASOCS mechanisms in general, in addition to LIA. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1106771. 	[Title] Analysis of the Convergence and Generalization of AA1 [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #34263. 	[Title] Word Perfect Corp. A TRANSFORMATION FOR IMPLEMENTING EFFICIENT DYNAMIC BACKPROPAGATION NEURAL NETWORKS [Abstract] Most Artificial Neural Networks (ANNs) have a fixed topology during learning, and often suffer from a number of shortcomings as a result. Variations of ANNs that use dynamic topologies have shown ability to overcome many of these problems. This paper introduces Location-Independent Transformations (LITs) as a general strategy for implementing distributed feedforward networks that use dynamic topologies (dynamic ANNs) efficiently in parallel hardware. A LIT creates a set of location-independent nodes, where each node computes its part of the network output independent of other nodes, using local information. This type of transformation allows efficient sup port for adding and deleting nodes dynamically during learning. In particular, this paper presents an LIT for standard Backpropagation with two layers of weights, and shows how dynamic extensions to Backpropagation can be supported. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #34266. 	[Title] A VLSI Implementation of a Parallel, Self-Organizing Learning Model [Abstract] This paper presents a VLSI implementation of the Priority Adaptive Self-Organizing Concurrent System (PASOCS) learning model that is built using a multi-chip module (MCM) substrate. Many current hardware implementations of neural network learning models are direct implementations of classical neural network structures|a large number of simple computing nodes connected by a dense number of weighted links. PASOCS is one of a class of ASOCS (Adaptive Self-Organizing Concurrent System) connectionist models whose overall goal is the same as classical neural networks models, but whose functional mechanisms differ significantly. This model has potential application in areas such as pattern recognition, robotics, logical inference, and dynamic control. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #45533. 	[Title] Priority ASOCS  ASOCS models have two significant advantages over other learning models: [Abstract] This paper presents an ASOCS (Adaptive Self-Organizing Concurrent System) model for massively parallel processing of incrementally defined rule systems in such areas as adaptive logic, robotics, logical inference, and dynamic control. An ASOCS is an adaptive network composed of many simple computing elements operating asynchronously and in parallel. An ASOCS can operate in either a data processing mode or a learning mode. During data processing mode, an ASOCS acts as a parallel hardware circuit. During learning mode, an ASOCS incorporates a rule expressed as a Boolean conjunction in a distributed fashion in time logarithmic in the number of rules. This paper proposes a learning algorithm and architecture for Priority ASOCS. This new ASOCS model uses rules with priorities. The new model has significant learning time and space complexity improvements over previous models. Non-von Neumann architectures such as neural networks attack the word-at-a-time bottleneck of traditional computing systems [1]. Neural networks learn input-output mappings using highly distributed processing and memory [10,11,12]. Their numerous simple processing elements with modifiable weighted links permit a high degree of parallelism. A typical neural network has fixed topology. It learns by modifying weighted links between nodes. A new class of connectionist architectures has been proposed called ASOCS (Adaptive Self-Organizing Concurrent Systems) [4,5]. ASOCS models support efficient computation through self-organized learning and parallel execution. Learning is done through the incremental presentation of rules and/or examples. ASOCS models learn by modifying their topology. Data types include Boolean and multi-state variables; recent models support analog variables. The model incorporates rules into an adaptive logic network in a parallel and self organizing fashion. In processing mode, ASOCS supports fully parallel execution on actual inputs according to the learned rules. The adaptive logic network acts as a parallel hardware circuit during execution, mapping n input boolean vectors into m output boolean vectors, in a combinatoric fashion. The overall philosophy of ASOCS follows the high level goals of current neural network models. However, the mechanisms of learning and execution vary significantly. The ASOCS logic network is topologically dynamic with the network growing to efficiently fit the specific application. Current ASOCS models are based on digital nodes. ASOCS also supports use of symbolic and heuristic learning mechanisms, thus combining the parallelism and distributed nature of connectionist computing with the potential power of AI symbolic learning. A proof of concept ASOCS chip has been developed [2]. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #368605. 	[Title] A Generalizing Adaptive Discriminant Network [Abstract] This paper overviews the AA1 (Adaptive Algorithm 1) model of ASOCS the (Adaptive Self - Organizing Concurrent Systems) approach. It also presents promising empirical generalization results of AA1 with actual data. AA1 is a topologically dynamic network which grows to fit the problem being learned. AA1 generalizes in a self-organizing fashion to a network which seeks to find features which discriminate between concepts. Convergence to a training set is both guaranteed and bounded linearly in time. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #192870. 	[Title] A Provably Convergent Dynamic Training Method for Multilayer Perceptron Networks [Abstract] This paper presents a new method for training multilayer perceptron networks called DMP1 (Dynamic Multilayer Perceptron 1). The method is based upon a divide and conquer approach which builds networks in the form of binary trees, dynamically allocating nodes and layers as needed. The individual nodes of the network are trained using a gentetic algorithm. The method is capable of handling real-valued inputs and a proof is given concerning its convergence properties of the basic model. Simulation results show that DMP1 performs favorably in comparison with other learning algorithms. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1153003. 	[Title] Eclectic Machine Learning [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1104495. 	[Title] The Potential of Prototype Styles of Generalization [Abstract] There are many ways for a learning system to generalize from training set data. This paper presents several generalization styles using prototypes in an attempt to provide accurate generalization on training set data for a wide variety of applications. These generalization styles are efficient in terms of time and space, and lend themselves well to massively parallel architectures. Empirical results of generalizing on several real-world applications are given, and these results indicate that the prototype styles of generalization presented have potential to provide accurate generalization for many applications. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #213246. 	[Title] Growing Layers of Perceptrons: Introducing the Extentron Algorithm [Abstract] vations of perceptrons: (1) when the perceptron learning algorithm cycles among hyperplanes, the hyperplanes may be compared to select one that gives a best split of the examples, and (2) it is always possible for the perceptron to build a hyper- plane that separates at least one example from all the rest. We describe the Extentron which grows multi-layer networks capable of distinguishing non- linearly-separable data using the simple perceptron rule for linear threshold units. The resulting algorithm is simple, very fast, scales well to large prob - lems, retains the convergence properties of the perceptron, and can be completely specified using only two parameters. Results are presented comparing the Extentron to other neural network paradigms and to symbolic learning systems. Please summarise all the given information.

Paper #87482 has the title `A Multi-Chip Module Implementation of a Neural Network  
` and the abstract `The requirement for dense interconnect in artificial neural network systems has led researchers to seek high-density interconnect technologies. This paper reports an implementation using multi-chip modules (MCMs) as the interconnect medium. The specific system described is a self-organizing, parallel, and dynamic learning model which requires a dense interconnect technology for effective implementation; this requirement is fulfilled by exploiting MCM technology. The ideas presented in this paper regarding an MCM implementation of artificial neural networks are versatile and can be adapted to apply to other neural network and connectionist models. 
`.
Question: Which category should Paper #87482 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #87482 has the title `A Multi-Chip Module Implementation of a Neural Network  
` and the abstract `The requirement for dense interconnect in artificial neural network systems has led researchers to seek high-density interconnect technologies. This paper reports an implementation using multi-chip modules (MCMs) as the interconnect medium. The specific system described is a self-organizing, parallel, and dynamic learning model which requires a dense interconnect technology for effective implementation; this requirement is fulfilled by exploiting MCM technology. The ideas presented in this paper regarding an MCM implementation of artificial neural networks are versatile and can be adapted to apply to other neural network and connectionist models. 
`.
Question: Which category should Paper #87482 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