528,745 research outputs found
Comparing Statistical Feature and Artificial Neural Networks for Control Chart Pattern Recognition: A Case Study
Control chart has been widely used for monitoring production process, especially in
evaluating the quality performance of a product. An uncontrolled process is usually known by
recognizing its chart pattern, and then performing some actions to overcome the problems. In high
speed production process, real-time data is recorded and plotted almost automatically, and the control
chart pattern needs to be recognized immediately for detecting any unusual process behavior. Neural
networks for automatic control chart recognition have been studied in detecting its pattern. In the field
of computer science, the performance of its automatic and fast recognition ability can be a substitution
for a conventional method by human. Some researchers even have developed newer algorithm to
increase the recognition process of this neural networks control chart. However, artificial approaches
have some difficulties in implementation, especially due to its sophisticated programming algorithm.
Another competing method, based on statistical feature also has been considered in recognition
process. Control chart is related to applied statistical method, so it is not unreasonable if statistical
properties are developed for its pattern recognition. Correlation coefficient, one of classic statistical
features, can be applied in control chart recognition. It is a simpler approach than the artificial one. In
this paper, the comparison between these two methods starts by evaluating the behavior of control
chart time series point, and measured for its closeness to some training data that are generated by
simulation and followed some unusual control chart pattern. For both methods, the performance is evaluated by comparing their ability in detecting the pattern of generated control chart points. As a sophisticated method, neural networks give better recognition ability. The statistical features method simply calculate the correlation coefficient, even with small differences in recognizing the generated pattern compared to neural networks, but provides easy interpretation to justify the unusual control chart pattern. Both methods are then applied in a case study and performances are then measured
On sample complexity for computational pattern recognition
In statistical setting of the pattern recognition problem the number of
examples required to approximate an unknown labelling function is linear in the
VC dimension of the target learning class. In this work we consider the
question whether such bounds exist if we restrict our attention to computable
pattern recognition methods, assuming that the unknown labelling function is
also computable. We find that in this case the number of examples required for
a computable method to approximate the labelling function not only is not
linear, but grows faster (in the VC dimension of the class) than any computable
function. No time or space constraints are put on the predictors or target
functions; the only resource we consider is the training examples.
The task of pattern recognition is considered in conjunction with another
learning problem -- data compression. An impossibility result for the task of
data compression allows us to estimate the sample complexity for pattern
recognition
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Predictive models for multibiometric systems
Recognizing a subject given a set of biometrics is a fundamental pattern recognition problem. This paper builds novel statistical models for multibiometric systems using geometric and multinomial distributions. These models are generic as they are only based on the similarity scores produced by a recognition system. They predict the bounds on the range of indices within which a test subject is likely to be present in a sorted set of similarity scores. These bounds are then used in the multibiometric recognition system to predict a smaller subset of subjects from the database as probable candidates for a given test subject. Experimental results show that the proposed models enhance the recognition rate beyond the underlying matching algorithms for multiple face views, fingerprints, palm prints, irises and their combinations
Offline Signature Verification by Combining Graph Edit Distance and Triplet Networks
Biometric authentication by means of handwritten signatures is a challenging
pattern recognition task, which aims to infer a writer model from only a
handful of genuine signatures. In order to make it more difficult for a forger
to attack the verification system, a promising strategy is to combine different
writer models. In this work, we propose to complement a recent structural
approach to offline signature verification based on graph edit distance with a
statistical approach based on metric learning with deep neural networks. On the
MCYT and GPDS benchmark datasets, we demonstrate that combining the structural
and statistical models leads to significant improvements in performance,
profiting from their complementary properties
Handwritten digit classification
Pattern recognition is one of the major challenges in statistics framework. Its goal is the feature extraction to classify the patterns into categories. A well-known example in this field is the handwritten digit recognition where digits have to be assigned into one of the 10 classes using some classification method. Our purpose is to present alternative classification methods based on statistical techniques. We show a comparison between a multivariate and a probabilistic approach, concluding that both methods provide similar results in terms of test-error rate. Experiments are performed on the known MNIST and USPS databases in binary-level image. Then, as an additional contribution we introduce a novel method to binarize images, based on statistical concepts associated to the written trace of the digitDigit, Classification, Images
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