Above are papers related to the following paper: [Paper_ID] Paper #248395. 	[Title] DIFFERENTIALLY GENERATED NEURAL NETWORK CLASSIFIERS ARE EFFICIENT [Abstract] Differential learning for statistical pattern classification is described in [5]; it is based on the classification figure-of-merit (CFM) objective function described in [9, 5]. We prove that differential learning is asymptotically efficient, guaranteeing the best generalization allowed by the choice of hypothesis class (see below) as the training sample size grows large, while requiring the least classifier complexity necessary for Bayesian (i.e., minimum probability-of-error) discrimination. Moreover, differential learning almost always guarantees the best generalization allowed by the choice of hypothesis class for small training sample sizes. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #58552. 	[Title] Differential theory of learning for efficient neural network pattern recognition [Abstract] We describe a new theory of differential learning by which a broad family of pattern classifiers (including many well-known neural network paradigms) can learn stochastic concepts efficiently. We describe the relationship between a classifier's ability to generalize well to unseen test examples and the efficiency of the strategy by which it learns. We list a series of proofs that differential learning is efficient in its information and computational resource requirements, whereas traditional probabilistic learning strategies are not. The proofs are illustrated by a simple example that lends itself to closed-form analysis. We conclude with an optical character recognition task for which three different types of differentially generated classifiers generalize significantly better than their probabilistically generated counterparts. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1113828. 	[Title] DIAGNOSING AND CORRECTING SYSTEM ANOMALIES WITH A ROBUST CLASSIFIER [Abstract] If a robust statistical model has been developed to classify the ``health'' of a system, a well-known Taylor series approximation technique forms the basis of a diagnostic/recovery procedure that can be initiated when the system's health degrades or fails altogether. This procedure determines a ranked set of probable causes for the degraded health state, which can be used as a prioritized checklist for isolating system anomalies and quantifying corrective action. The diagnostic/recovery procedure is applicable to any classifier known to be robust; it can be applied to both neural network and traditional parametric pattern classifiers generated by a supervised learning procedure in which an empirical risk/benefit measure is optimized. We describe the procedure mathematically and demonstrate its ability to detect and diagnose the cause(s) of faults in NASA's Deep Space Communications Complex at Goldstone, California. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #910. 	[Title] Learning networks for face analysis and synthesis [Abstract] This paper presents an overview of the face-related projects in our group. The unifying theme underlying our work is the use of example-based learning methods for both analyzing and synthesizing face images. We label the example face images (and for the problem of face detection, "near miss" faces as well) with descriptive parameters for pose, expression, identity, and face vs. non-face. Then, by using example-based learning techniques, we develop networks for performing analysis tasks such as pose and expression estimation, face recognition, and face detection in cluttered scenes. In addition to these analysis applications, we show how the example-based technique can also be used as a novel method for image synthesis that is for computer graphics. Please summarise all the given information.

Paper #248395 has the title `DIFFERENTIALLY GENERATED NEURAL NETWORK CLASSIFIERS ARE EFFICIENT  
` and the abstract `Differential learning for statistical pattern classification is described in [5]; it is based on the classification figure-of-merit (CFM) objective function described in [9, 5]. We prove that differential learning is asymptotically efficient, guaranteeing the best generalization allowed by the choice of hypothesis class (see below) as the training sample size grows large, while requiring the least classifier complexity necessary for Bayesian (i.e., minimum probability-of-error) discrimination. Moreover, differential learning almost always guarantees the best generalization allowed by the choice of hypothesis class for small training sample sizes. 
`.
Question: Which category should Paper #248395 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #248395 has the title `DIFFERENTIALLY GENERATED NEURAL NETWORK CLASSIFIERS ARE EFFICIENT  
` and the abstract `Differential learning for statistical pattern classification is described in [5]; it is based on the classification figure-of-merit (CFM) objective function described in [9, 5]. We prove that differential learning is asymptotically efficient, guaranteeing the best generalization allowed by the choice of hypothesis class (see below) as the training sample size grows large, while requiring the least classifier complexity necessary for Bayesian (i.e., minimum probability-of-error) discrimination. Moreover, differential learning almost always guarantees the best generalization allowed by the choice of hypothesis class for small training sample sizes. 
`.
Question: Which category should Paper #248395 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