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 #143801. 	[Title] Data Exploration with Reflective Adaptive Models [Abstract]  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 #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 #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 #1365. 	[Title] Cholinergic suppression of transmission may allow combined associative memory function and self-organization in the neocortex. [Abstract] Selective suppression of transmission at feedback synapses during learning is proposed as a mechanism for combining associative feedback with self-organization of feedforward synapses. Experimental data demonstrates cholinergic suppression of synaptic transmission in layer I (feedback synapses), and a lack of suppression in layer IV (feed-forward synapses). A network with this feature uses local rules to learn mappings which are not linearly separable. During learning, sensory stimuli and desired response are simultaneously presented as input. Feedforward connections form self-organized representations of input, while suppressed feedback connections learn the transpose of feedfor-ward connectivity. During recall, suppression is removed, sensory input activates the self-organized representation, and activity generates the learned response. 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 #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 #284025. 	[Title] The Pandemonium System of Reflective Agents [Abstract] In IEEE Transactions on Neural Networks, 7(1):97-106, 1996 Also available as GMD report #794 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 #1152958 has the title `nan` and the 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. 
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Question: Which category should Paper #1152958 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1152958 has the title `nan` and the 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. 
`.
Question: Which category should Paper #1152958 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