Our first comment is on the optimization of the model parameter τ controlling the amount of noise in the augmented bootstrap method. In a supervised prediction problem, τ can and should be optimized using, e.g., a cross-validation (CV) procedure, as suggested by the authors. If the prediction accuracy is itself evaluated by cross-validation or a related approach, this yields a nested cross-vali...

We consider a priori generalization bounds developed in terms of cross-validation estimates and the stability of learners. In particular, we first derive an exponential Efron-Stein type tail inequality for the concentration of a general function of n independent random variables. Next, under some reasonable notion of stability, we use this exponential tail bound to analyze the concentration of ...

Pattern classification is a core research area and a main task in pattern recognition. A classifier induced by machine learning algorithms maps an unlabeled instance to a label using internal data structures. In this paper we experiment first by changing the k value of nearest neighbors from 3 to 15 and compare the accuracy of two classifiers on various training and test sets. The results show ...

Tomato Leaf Disease is one of the common things for farmers in growing tomatoes. Tomatoes are popular crops that can grow low and high areas but susceptible to disease. For this reason, take precautions by looking at characteristics texture tomato leaves. However, requires more time money a long process. One efforts be made classify leaf diseases. This research aims using Support Vector Machine...

Cross-validation is an established technique for estimating the accuracy of a classifier and is normally performed either using a number of random test/train partitions of the data, or using kfold cross-validation. We present a technique for calculating the complete cross-validation for nearest-neighbor classifiers: i.e., averaging over all desired test/train partitions of data. This technique ...

k-fold cross validation is a popular practical method to get a good estimate of the error rate of a learning algorithm. Here, the set of examples is first partitioned into k equal-sized folds. Each fold acts as a test set for evaluating the hypothesis learned on the other k − 1 folds. The average error across the k hypotheses is used as an estimate of the error rate. Although widely used, espec...