A trainable feature extractor for handwritten digit recognition
نویسندگان
چکیده
منابع مشابه
A trainable feature extractor for handwritten digit recognition
This article focusses on the problems of feature extraction and the recognition of handwritten digits. A trainable feature extractor based on the LeNet5 convolutional neural network architecture is introduced to solve the first problem in a black box scheme without prior knowledge on the data. The classification task is performed by Support Vector Machines to enhance the generalization ability ...
متن کاملFeature Subset Selection Using Genetic Algorithms for Handwritten Digit Recognition
In this paper two approaches of genetic algorithm for feature subset selection are compared. The first approach considers a simple genetic algorithm (SGA) while the second one takes into account an iterative genetic algorithm (IGA) which is claimed to converge faster than SGA. Initially, we present an overview of the system to be optimized and the methodology applied in the experiments as well....
متن کاملPersian Handwritten Digit Recognition Using Particle Swarm Probabilistic Neural Network
Handwritten digit recognition can be categorized as a classification problem. Probabilistic Neural Network (PNN) is one of the most effective and useful classifiers, which works based on Bayesian rule. In this paper, in order to recognize Persian (Farsi) handwritten digit recognition, a combination of intelligent clustering method and PNN has been utilized. Hoda database, which includes 80000 P...
متن کاملNeocognitron for handwritten digit recognition
The author previously proposed a neural network model neocognitron for robust visual pattern recognition. This paper proposes an improved version of the neocognitron and demonstrates its ability using a large database of handwritten digits (ETL1). To improve the recognition rate of the neocognitron, several modi0cations have been applied: such as, the inhibitory surround in the connections from...
متن کاملError-corrective Feature Extraction in Handwritten Digit Recognition
The idea of feedback and error-correction is central in neurally motivated classiication algorithms. Most of the neural models, however, take the preceding feature extraction stage as given. Unfortunately, essential information may be unrecoverably lost in the feature extraction phase, leading to degraded classiication accuracy. Enhanced overall classiication performance would follow, if the ne...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Pattern Recognition
سال: 2007
ISSN: 0031-3203
DOI: 10.1016/j.patcog.2006.10.011