Above are papers related to the following paper: [Paper_ID] Paper #213279. 	[Title] Constraint Tangent Distance for On-line Character Recognition [Abstract] In on-line character recognition we can observe two kinds of intra-class variations: small geometric deformations and completely different writing styles. We propose a new approach to deal with these problems by defining an extension of tangent distance [9], well known in off-line character recognition. The system has been implemented with a k-nearest neighbor classifier and a so called diabolo classifier [6] respectively. Both classifiers are invariant under transformations like rotation, scale or slope and can deal with variations in stroke order and writing direction. Results are presented for our digit database with more than 200 writers. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #395553. 	[Title] Mixtures of Probabilistic Principal Component Analysers [Abstract] Principal component analysis (PCA) is one of the most popular techniques for processing, compressing and visualising data, although its effectiveness is limited by its global linearity. While nonlinear variants of PCA have been proposed, an alternative paradigm is to capture data complexity by a combination of local linear PCA projections. However, conventional PCA does not correspond to a probability density, and so there is no unique way to combine PCA models. Previous attempts to formulate mixture models for PCA have therefore to some extent been ad hoc. In this paper, PCA is formulated within a maximum-likelihood framework, based on a specific form of Gaussian latent variable model. This leads to a well-defined mixture model for probabilistic principal component analysers, whose parameters can be determined using an EM algorithm. We discuss the advantages of this model in the context of clustering, density modelling and local dimensionality reduction, and we demonstrate its application to image compression and handwritten digit recognition. 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 #400356. 	[Title] Factor Analysis Using Delta-Rule Wake-Sleep Learning [Abstract] Technical Report No. 9607, Department of Statistics, University of Toronto We describe a linear network that models correlations between real-valued visible variables using one or more real-valued hidden variables a factor analysis model. This model can be seen as a linear version of the Helmholtz machine, and its parameters can be learned using the wake-sleep method, in which learning of the primary generative model is assisted by a recognition model, whose role is to fill in the values of hidden variables based on the values of visible variables. The generative and recognition models are jointly learned in wake and sleep phases, using just the delta rule. This learning procedure is comparable in simplicity to Oja's version of Hebbian learning, which produces a somewhat different representation of correlations in terms of principal components. We argue that the simplicity of wake-sleep learning makes factor analysis a plau sible alternative to Hebbian learning as a model of activity-dependent cortical plasticity. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1153861. 	[Title] Data Mining for Association Rules with Unsupervised Neural Networks [Abstract] results for Gaussian mixture models and factor analysis are discussed. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1120777. 	[Title] Modelling the Manifolds of Images of Handwritten Digits [Abstract]  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 #1152490. 	[Title] Data Mining for Association Rules with Unsupervised Neural Networks [Abstract] results for Gaussian mixture models and factor analysis are discussed. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #2702. 	[Title] In  Fast Non-Linear Dimension Reduction [Abstract] We present a fast algorithm for non-linear dimension reduction. The algorithm builds a local linear model of the data by merging PCA with clustering based on a new distortion measure. Experiments with speech and image data indicate that the local linear algorithm produces encodings with lower distortion than those built by five layer auto-associative networks. The local linear algorithm is also more than an order of magnitude faster to train. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #156977. 	[Title] The EM Algorithm for Mixtures of Factor Analyzers [Abstract] Technical Report CRG-TR-96-1 May 21, 1996 (revised Feb 27, 1997) Abstract Factor analysis, a statistical method for modeling the covariance structure of high dimensional data using a small number of latent variables, can be extended by allowing different local factor models in different regions of the input space. This results in a model which concurrently performs clustering and dimensionality reduction, and can be thought of as a reduced dimension mixture of Gaussians. We present an exact Expectation-Maximization algorithm for fitting the parameters of this mixture of factor analyzers. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #107569. 	[Title] Using generative models for handwritten digit recognition [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #197783. 	[Title] Adaptive Boosting of Neural Networks for Character Recognition [Abstract] Technical Report #1072, D epartement d'Informatique et Recherche Op erationnelle, Universit e de Montr eal Abstract Boosting is a general method for improving the performance of any learning algorithm that consistently generates classifiers which need to perform only slightly better than random guessing. A recently proposed and very promising boosting algorithm is AdaBoost [5]. It has been applied with great success to several benchmark machine learning problems using rather simple learning algorithms [4], in particular decision trees [1, 2, 6]. In this paper we use AdaBoost to improve the performances of neural networks applied to character recognition tasks. We compare training methods based on sampling the training set and weighting the cost function. Our system achieves about 1.4% error on a data base of online handwritten digits from more than 200 writers. Adaptive boosting of a multi-layer network achieved 2% error on the UCI Letters offline characters data set. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #2653. 	[Title] A decision-theoretic generalization of on-line learning and an application to boosting how the weight-update rule [Abstract] We consider the problem of dynamically apportioning resources among a set of options in a worst-case on-line framework. The model we study can be interpreted as a broad, abstract extension of the well-studied on-line prediction model to a general decision-theoretic setting. We show that the multiplicative weight-update rule of Littlestone and Warmuth [10] can be adapted to this model yielding bounds that are slightly weaker in some cases, but applicable to a considerably more general class of learning problems. We show how the resulting learning algorithm can be applied to a variety of problems, including gambling, multiple-outcome prediction, repeated games and prediction of points in R n Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #2654. 	[Title] Experiments with a New Boosting Algorithm [Abstract] In an earlier paper, we introduced a new boosting algorithm called AdaBoost which, theoretically, can be used to significantly reduce the error of any learning algorithm that consistently generates classifiers whose performance is a little better than random guessing. We also introduced the related notion of a pseudo-loss which is a method for forcing a learning algorithm of multi-label concepts to concentrate on the labels that are hardest to discriminate. In this paper, we describe experiments we carried out to assess how well AdaBoost with and without pseudo-loss, performs on real learning problems. We performed two sets of experiments. The first set compared boosting to Breiman's bagging method when used to aggregate various classifiers (including decision trees and single attribute-value tests). We compared the performance of the two methods on a collection of machine-learning benchmarks. In the second set of experiments, we studied in more detail the performance of boosting using a nearest-neighbor classifier on an OCR problem. Please summarise all the given information.

Paper #213279 has the title `Constraint Tangent Distance for On-line Character Recognition  
` and the abstract `In on-line character recognition we can observe two kinds of intra-class variations: small geometric deformations and completely different writing styles. We propose a new approach to deal with these problems by defining an extension of tangent distance [9], well known in off-line character recognition. The system has been implemented with a k-nearest neighbor classifier and a so called diabolo classifier [6] respectively. Both classifiers are invariant under transformations like rotation, scale or slope and can deal with variations in stroke order and writing direction. Results are presented for our digit database with more than 200 writers. 
`.
Question: Which category should Paper #213279 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #213279 has the title `Constraint Tangent Distance for On-line Character Recognition  
` and the abstract `In on-line character recognition we can observe two kinds of intra-class variations: small geometric deformations and completely different writing styles. We propose a new approach to deal with these problems by defining an extension of tangent distance [9], well known in off-line character recognition. The system has been implemented with a k-nearest neighbor classifier and a so called diabolo classifier [6] respectively. Both classifiers are invariant under transformations like rotation, scale or slope and can deal with variations in stroke order and writing direction. Results are presented for our digit database with more than 200 writers. 
`.
Question: Which category should Paper #213279 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