Results 1  10
of
26,874
2010 International Conference on Pattern Recognition CUDA implementation of deformable pattern recognition and its application to MNIST handwritten digit database
"... Abstract—In this study we propose a deformable pattern recognition method with CUDA implementation. In order to achieve the proper correspondence between foreground pixels of input and prototype images, a pair of distance maps are generated from input and prototype images, whose pixel values are giv ..."
Abstract
 Add to MetaCart
that the proposed method with the knearest neighbor classifier gives the error rate of 0.57 % for the MNIST handwritten digit database. Keywordshandwritten character recognition; displacement computation; graphics processing unit; compute unified device architecture; I.
Distance metric learning for large margin nearest neighbor classification
 In NIPS
, 2006
"... We show how to learn a Mahanalobis distance metric for knearest neighbor (kNN) classification by semidefinite programming. The metric is trained with the goal that the knearest neighbors always belong to the same class while examples from different classes are separated by a large margin. On seven ..."
Abstract

Cited by 685 (15 self)
 Add to MetaCart
. On seven data sets of varying size and difficulty, we find that metrics trained in this way lead to significant improvements in kNN classification—for example, achieving a test error rate of 1.3 % on the MNIST handwritten digits. As in support vector machines (SVMs), the learning problem reduces to a
THE MNIST DATABASE
"... The MNIST database of handwritten digits, available from this page, has a training set of 60,000 examples, and a test set of 10,000 examples. It is a subset of a larger set available from NIST. The digits have been sizenormalized and centered in a fixedsize image. It is a good database for people ..."
Abstract
 Add to MetaCart
The MNIST database of handwritten digits, available from this page, has a training set of 60,000 examples, and a test set of 10,000 examples. It is a subset of a larger set available from NIST. The digits have been sizenormalized and centered in a fixedsize image. It is a good database for people
A Digital Signature Scheme Secure Against Adaptive ChosenMessage Attacks
, 1995
"... We present a digital signature scheme based on the computational diculty of integer factorization. The scheme possesses the novel property of being robust against an adaptive chosenmessage attack: an adversary who receives signatures for messages of his choice (where each message may be chosen in a ..."
Abstract

Cited by 985 (43 self)
 Add to MetaCart
We present a digital signature scheme based on the computational diculty of integer factorization. The scheme possesses the novel property of being robust against an adaptive chosenmessage attack: an adversary who receives signatures for messages of his choice (where each message may be chosen
Shape Matching and Object Recognition Using Shape Contexts
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2001
"... We present a novel approach to measuring similarity between shapes and exploit it for object recognition. In our framework, the measurement of similarity is preceded by (1) solv ing for correspondences between points on the two shapes, (2) using the correspondences to estimate an aligning transform ..."
Abstract

Cited by 1787 (21 self)
 Add to MetaCart
prototype shape that is maximally similar to that in the image. Results are presented for silhouettes, trademarks, handwritten digits and the COIL dataset.
Gradientbased learning applied to document recognition
 Proceedings of the IEEE
, 1998
"... Multilayer neural networks trained with the backpropagation algorithm constitute the best example of a successful gradientbased learning technique. Given an appropriate network architecture, gradientbased learning algorithms can be used to synthesize a complex decision surface that can classify hi ..."
Abstract

Cited by 1465 (84 self)
 Add to MetaCart
highdimensional patterns, such as handwritten characters, with minimal preprocessing. This paper reviews various methods applied to handwritten character recognition and compares them on a standard handwritten digit recognition task. Convolutional neural networks, which are specifically designed
A fast learning algorithm for deep belief nets
 Neural Computation
, 2006
"... We show how to use “complementary priors ” to eliminate the explaining away effects that make inference difficult in denselyconnected belief nets that have many hidden layers. Using complementary priors, we derive a fast, greedy algorithm that can learn deep, directed belief networks one layer at a ..."
Abstract

Cited by 930 (51 self)
 Add to MetaCart
very good generative model of the joint distribution of handwritten digit images and their labels. This generative model gives better digit classification than the best discriminative learning algorithms. The lowdimensional manifolds on which the digits lie are modelled by long ravines in the free
Typesetting Concrete Mathematics
 TUGBOAT
, 1989
"... ... tried my best to make the book mathematically interesting, but I also knew that it would be typographically interestingbecause it would be the first major use of a new typeface by Hermann Zapf, commissioned by the American Mathematical Society. This typeface, called AMS Euler, had been carefull ..."
Abstract

Cited by 664 (1 self)
 Add to MetaCart
carefully digitized and put into METAFONT form by Stanford's digital typography students [a]; but it had not yet been "tuned up " for real applications. My new book served as an ideal test case, because (1) it involved a great variety of mathematical formulas; (2) I was highly motivated
Large Margin Classification Using the Perceptron Algorithm
 Machine Learning
, 1998
"... We introduce and analyze a new algorithm for linear classification which combines Rosenblatt 's perceptron algorithm with Helmbold and Warmuth's leaveoneout method. Like Vapnik 's maximalmargin classifier, our algorithm takes advantage of data that are linearly separable with large ..."
Abstract

Cited by 518 (2 self)
 Add to MetaCart
algorithm, and some variants of it, for classifying images of handwritten digits. The performance of our algorithm is close to, but not as good as, the performance of maximalmargin classifiers on the same problem, while saving significantly on computation time and programming effort. 1 Introduction One
Results 1  10
of
26,874