Improving the Efficiency of Counting Defects by Learning RBF Nets with MAD Loss
نویسنده
چکیده
The method of using a lateral histogram for evaluating the number of holes (e.g., defects) from images is known to be fast but rather inaccurate. Our aim is to propose a method of improving its performance by learning, but keeping the speed of the original method. This task is accomplished by considering a multiclass pattern recognition problem with linearly ordered labels and a loss function, which measures absolute deviations of decisions from true classes.
منابع مشابه
MAD Loss in Pattern Recognition and RBF Learning
We consider a multi-class pattern recognition problem with linearly ordered labels and a loss function, which measures absolute deviations of decisions from true classes. In the bayesian setting the optimal decision rule is shown to be the median of a posteriori class probabilities. Then, we propose three approaches to constructing an empirical decision rule, based on a learning sequence. Our s...
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