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in this paper, a smart method is designed in order to classify healthy and illness ducks using their emission voice. for this purpose, firstly, the birds based on their healthy condition are divided into the different categories and then their voices are saved using a microphone and data acquisition card. gained signals were transformed from time-domain signal to frequency domain using fast fou...

We address the problem of cool blog classification using only positive and unlabeled examples. We propose an algorithm, called PUB, that exploits the information of unlabeled data together with the positive examples to predict whether the unseen blogs are cool or not. The algorithm uses the weighting technique to assign a weight to each unlabeled example which is assumed to be negative in the t...

In recent work Long and Servedio [LS05] presented a “martingale boosting” algorithm that works by constructing a branching program over weak classifiers and has a simple analysis based on elementary properties of random walks. [LS05] showed that this martingale booster can tolerate random classification noise when it is run with a noise-tolerant weak learner; however, a drawback of the algorith...

This paper describes how to make the problem of binary classification amenable to quantum computing. A formulation is employed in which the binary classifier is constructed as a thresholded linear superposition of a set of weak classifiers. The weights in the superposition are optimized in a learning process that strives to minimize the training error as well as the number of weak classifiers u...

Boosting-based methods have recently led to the state-of-the-art face detection systems. In these systems, weak classifiers to be boosted are based on simple, local, Haar-like features. However, it can be empirically observed that in later stages of the boosting process, the non-face examples collected by bootstrapping become very similar to the face examples, and the classification error of Ha...

Discriminative learning, or learning for classification, is a common learning task that has been addressed in a variety of frameworks. One approach is to design a complex classifier, such as a support vector machine, that explicitly minimizes classification error. Alternatively, an ensemble of weak classifiers can be trained using boosting [4]. However, in some situations it may be desirable to...

In classification tasks it may be wise to combine observations from different sources. In this paper, to obtain classification systems with both good generalization performance and efficiency in space and time, a learning vector quantization learning method based on combinations of weak classifiers is proposed. The weak classifiers are generated using automatic elimination of redundant hidden l...