Above are papers related to the following paper: [Paper_ID] Paper #1118120. 	[Title] DNA: A New ASOCS Model With Improved Implementation Potential [Abstract] A new class of highspeed, self-adaptive, massively parallel computing models called ASOCS (Adaptive Self-Organizing Concurrent Systems) has been proposed. Current analysis suggests that there may be problems implementing ASOCS models in VLSI using the hierarchical network structures originally proposed. The problems are not inherent in the models, but rather in the technology used to implement them. This has led to the development of a new ASOCS model called DNA (Discriminant-Node ASOCS) that does not depend on a hierarchical node structure for success. Three areas of the DNA model are briefly discussed in this paper: DNA's flexible nodes, how DNA overcomes problems other models have allocating unused nodes, and how DNA operates during processing and learning. 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 #42156. 	[Title] Models of Parallel Adaptive Logic [Abstract] This paper overviews a proposed architecture for adaptive parallel logic referred to as ASOCS (Adaptive Self-Organizing Concurrent System). The ASOCS approach is based on an adaptive network composed of many simple computing elements which operate in a parallel asynchronous fashion. Problem specification is given to the system by presenting if-then rules in the form of boolean conjunctions. Rules are added incrementally and the system adapts to the changing rule-base. Adaptation and data processing form two separate phases of operation. During processing the system acts as a parallel hardware circuit. The adaptation process is distributed amongst the computing elements and efficiently exploits parallelism. Adaptation is done in a self-organizing fashion and takes place in time linear with the depth of the network. This paper summarizes the overall ASOCS concept and overviews three specific architectures. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1116594. 	[Title] ASOCS: A Multilayered Connectionist Network with Guaranteed Learning of Arbitrary Mappings [Abstract] This paper reviews features of a new class of multilayer connectionist architectures known as ASOCS (Adaptive Self-Organizing Concurrent Systems). ASOCS is similar to most decision-making neural network models in that it attempts to learn an adaptive set of arbitrary vector mappings. However, it differs dramatically in its mechanisms. ASOCS is based on networks of adaptive digital elements which self-modify using local information. Function specification is entered incrementally by use of rules, rather than complete input-output vectors, such that a processing network is able to extract critical features from a large environment and give output in a parallel fashion. Learning also uses parallelism and self-organization such that a new rule is completely learned in time linear with the depth of the network. The model guarantees learning of any arbitrary mapping of boolean input-output vectors. The model is also stable in that learning does not erase any previously learned mappings except those explicitly contradicted. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1152179. 	[Title] Digital Neural Networks [Abstract] Demands for applications requiring massive parallelism in symbolic environments have given rebirth to research in models labeled as neura l networks. These models are made up of many simple nodes which are highly interconnected such that computation takes place as data flows amongst the nodes of the network. To present, most models have proposed nodes based on simple analog functions, where inputs are multiplied by weights and summed, the total then optionally being transformed by an arbitrary function at the node. Learning in these systems is accomplished by adjusting the weights on the input lines. This paper discusses the use of digital (boolean) nodes as a primitive building block in connectionist systems. Digital nodes naturally engender new paradigms and mechanisms for learning and processing in connectionist networks. The digital nodes are used as the basic building block of a class of models called ASOCS (Adaptive Self-Organizing Concurrent Systems). These models combine massive parallelism with the ability to adapt in a self-organizing fashion. Basic features of standard neural network learning algorithms and those proposed using digital nodes are compared and contrasted. The latter mechanisms can lead to vastly improved efficiency for many applications. Please summarise all the given information.

Paper #1118120 has the title `DNA: A New ASOCS Model With Improved Implementation Potential  
` and the abstract `A new class of highspeed, self-adaptive, massively parallel computing models called ASOCS (Adaptive Self-Organizing Concurrent Systems) has been proposed. Current analysis suggests that there may be problems implementing ASOCS models in VLSI using the hierarchical network structures originally proposed. The problems are not inherent in the models, but rather in the technology used to implement them. This has led to the development of a new ASOCS model called DNA (Discriminant-Node ASOCS) that does not depend on a hierarchical node structure for success. Three areas of the DNA model are briefly discussed in this paper: DNA's flexible nodes, how DNA overcomes problems other models have allocating unused nodes, and how DNA operates during processing and learning. 
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Question: Which category should Paper #1118120 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1118120 has the title `DNA: A New ASOCS Model With Improved Implementation Potential  
` and the abstract `A new class of highspeed, self-adaptive, massively parallel computing models called ASOCS (Adaptive Self-Organizing Concurrent Systems) has been proposed. Current analysis suggests that there may be problems implementing ASOCS models in VLSI using the hierarchical network structures originally proposed. The problems are not inherent in the models, but rather in the technology used to implement them. This has led to the development of a new ASOCS model called DNA (Discriminant-Node ASOCS) that does not depend on a hierarchical node structure for success. Three areas of the DNA model are briefly discussed in this paper: DNA's flexible nodes, how DNA overcomes problems other models have allocating unused nodes, and how DNA operates during processing and learning. 
`.
Question: Which category should Paper #1118120 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