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Optical Recognition of Handwritten Digits Data Set

Below are papers that cite this data set, with context shown. Papers were automatically harvested and associated with this data set, in collaboration with

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Ken Tang and Ponnuthurai N. Suganthan and Xi Yao and A. Kai Qin. Linear dimensionalityreduction using relevance weighted LDA. School of Electrical and Electronic Engineering Nanyang Technological University. 2005.

is generated from landsat multi-spectral scanner image data. It has 36 dimensions, 4435 training samples and 2000 testing samples belonging to 6 classes. Optdigits. This is a 60-dimensional data set on optical recognition of 10 handwritten digits. It has separate training and testing sets with 3823 and 1797 samples, respectively. Vehicle. This data set involves classification of a given

Claudio Gentile. A New Approximate Maximal Margin Classification Algorithm. NIPS. 2000.

The 214 Approximate Maximal Margin Classification real-world datasets are well-known Optical Character Recognition (OCR) benchmarks. On these datasets we followed the experimental setting described by Cortes and Vapnik (1995), Freund and Schapire (1999), Li and Long

Ethem Alpaydin. Combined 5 x 2 cv F Test for Comparing Supervised Classification Learning Algorithms. Neural Computation, 11. 1999.

2 no VOWEL 2 no ODR 8 yes DIGIT 7 yes PEN 10 yes changing the numerator where we compare a single layer perceptron (LP) with a multilayer perceptron with one hidden layer (MLP). ODR, DIGIT are two datasets on optical handwritten digit recognition and PEN is on pen-based handwritten digit recognition. These three datasets are available from the author. The other datasets are from the UCI repository

Stephen D. Bay. Nearest neighbor classification from multiple feature subsets. Intell. Data Anal, 3. 1999.

or disjoint) in combination with the CNN classifier to edit and reduce the prototypes. He also reported improvements (on five domains from the UCI repository and one optical character recognition dataset) over the baseline NN classifier if the training sets were sufficiently small and thus able to generate diverse classifiers. It is important to note that both of Alpaydin's and Skalak's work differ

Erick Cantú-Paz and Chandrika Kamath. Using Evolutionary Algorithms to Induce Oblique Decision Trees. Center for Applied Scientific Computing Lawrence Livermore National Laboratory.

we experimented with the optical digit recognition data set, which is also available at UCI's ML repository. This data set has 3823 instances in a training set and 1797 in a testing set; each instance is described by 64 numeric attributes. The objective is

Ayhan Demiriz and Kristin P. Bennett and John Shawe and I. Nouretdinov V.. Linear Programming Boosting via Column Generation. Dept. of Decision Sciences and Eng. Systems, Rensselaer Polytechnic Institute.

with missing values. The default handling in C4.5 has been used for missing values. USPS and Optdigits are optical character recognition datasets. USPS has 256 dimensions without missing values. Out of 7291 original training points, we use 1822 points as training data and the other 5469 as validation data. There are 2007 test points.

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