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 #949217. 	[Title] Comparison of Neural and Statistical Classifiers| Theory and Practice [Abstract] Research Reports A13 January 1996 Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #4330. 	[Title] Hierarchical Mixtures of Experts and the EM Algorithm [Abstract] We present a tree-structured architecture for supervised learning. The statistical model underlying the architecture is a hierarchical mixture model in which both the mixture coefficients and the mixture components are generalized linear models (GLIM's). Learning is treated as a maximum likelihood problem; in particular, we present an Expectation-Maximization (EM) algorithm for adjusting the parameters of the architecture. We also develop an on-line learning algorithm in which the parameters are updated incrementally. Comparative simulation results are presented in the robot dynamics domain. This report describes research done at the Dept. of Brain and Cognitive Sciences, the Center for Biological and Computational Learning, and the Artificial Intelligence Laboratory of the Massachusetts Institute of Technology. Support for CBCL is provided in part by a grant from the NSF (ASC-9217041). Support for the laboratory's artificial intelligence research is provided in part by the Advanced Research Projects Agency of the Dept. of Defense. The authors were supported by a grant from the McDonnell-Pew Foundation, by a grant from ATR Human Information Processing Research Laboratories, by a grant from Siemens Corporation, by by grant IRI-9013991 from the National Science Foundation, by grant N00014-90-J-1942 from the Office of Naval Research, and by NSF grant ECS-9216531 to support an Initiative in Intelligent Control at MIT. Michael I. Jordan is a NSF Presidential Young Investigator. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #12330. 	[Title] Recognizing Handwritten Digits Using Mixtures of Linear Models [Abstract] We construct a mixture of locally linear generative models of a collection of pixel-based images of digits, and use them for recognition. Different models of a given digit are used to capture different styles of writing, and new images are classified by evaluating their log-likelihoods under each model. We use an EM-based algorithm in which the M-step is computationally straightforward principal components analysis (PCA). Incorporating tangent-plane information [12] about expected local deformations only requires adding tangent vectors into the sample covariance matrices for the PCA, and it demonstrably improves performance. 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 #340078. 	[Title] Evaluation of Pattern Classifiers for Fingerprint and OCR Applications [Abstract] In this paper we evaluate the classification accuracy of four statistical and three neural network classifiers for two image based pattern classification problems. These are fingerprint classification and optical character recognition (OCR) for isolated handprinted digits. The evaluation results reported here should be useful for designers of practical systems for these two important commercial applications. For the OCR problem, the Karhunen-Loeve (K-L) transform of the images is used to generate the input feature set. Similarly for the fingerprint problem, the K-L transform of the ridge directions is used to generate the input feature set. The statistical classifiers used were Euclidean minimum distance, quadratic minimum distance, normal, and k-nearest neighbor. The neural network classifiers used were multilayer perceptron, radial basis function, and probabilistic. The OCR data consisted of 7,480 digit images for training and 23,140 digit images for testing. The fingerprint data consisted of 2,000 training and 2,000 testing images. In addition to evaluation for accuracy, the multilayer perceptron and radial basis function networks were evaluated for size and generalization capability. For the evaluated datasets the best accuracy obtained for either problem was provided by the probabilistic neural network, where the minimum classification error was 2.5% for OCR and 7.2% for fingerprints. 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 #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 #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 #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 #12638. 	[Title] Soft Classification, a.k.a. Risk Estimation, via Penalized Log Likelihood and Smoothing Spline Analysis of Variance [Abstract]  Please summarise all the given information.

Paper #284025 has the title `The Pandemonium System of Reflective Agents  
` and the abstract `In IEEE Transactions on Neural Networks, 7(1):97-106, 1996 Also available as GMD report #794 
`.
Question: Which category should Paper #284025 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #284025 has the title `The Pandemonium System of Reflective Agents  
` and the abstract `In IEEE Transactions on Neural Networks, 7(1):97-106, 1996 Also available as GMD report #794 
`.
Question: Which category should Paper #284025 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