The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
In this paper, a novel multiple-kernel learning (MKL) algorithm is proposed for classification of hyperspectral images. The goal of classification is to acquire the class label of each pixel. The land covers is linearly separable in the kernel space spanned by class labels (ideal kernel). The ideal kernel is used as the optimization objective of our proposed MKL algorithm. Linear programming (LP)...
Imbalanced data classification problem has always been a hotspot in the field of machine learning research. Pointing to the overfitting and noise problems of oversampling algorithm when synthesizing new minority class samples, the current study proposed a stacked denoising autoencoder neural network (SDAE) algorithm based on cost-sensitive oversampling, combining the cost-sensitive learning with denoising...
ART2 is a kind of self-organizing neural network which is based on adaptive resonance theory. It carries out the recognition by using competive learning and self-steady mechanism, and can learn by itself in dynamic environment with noise and without supervision. Its learning process can recognize learned models fastly and be adapted to new unknown objects rapidly. SAR ATR (Synthetic Aperture Radar...
The data mining technology is more and more widely used. How to construct a Bayesian belief network has been discussed in many different ways. As one of the classical algorithms, the branch and bound technique based on the minimum description length principle has been proposed by Joe Suzuki in 1998. But one of the most important premises of the B&B Technique is that an attributes' dependence ordering...
The data mining technology is more and more widely used in the telecom industry. But telecom data set always includes instances with missing values. Besides, many data mining models are sensitive for the missing value and distortion. Estimating missing values becomes an inherent problem. To address the problem, A prediction method is proposed for the missing value based on the BP neural network and...
The goal of ranking is to learn a real-valued ranking function that induces a ranking or ordering over an instance space. A learning algorithm is stable if the algorithm satisfies the hypothesis that the output of the algorithm varies in a limited way in response to small changes made to the training set. This paper studies the `almost everywhere' stability of ranking algorithms, notions of strong...
In this paper, the stability of ranking algorithms is studied by adopting a strategy which adjusts the sample set by deleting one or two element from it. Relationship between uniform loss stability and uniform score stability is investigated. A sufficient condition for uniform score stability is given. The result of our work shows that if a uniform score stability ranking algorithm use γ ranking loss,...
Based on rough set and basic theory of data fusion, the data fusion algorithm combining rough set theory and BP neural network is studied. Since rough set theory can effectively simplify information, cut down the tagged dimension . This paper will be rough set theory and neural networks combined, using channel capacity of knowledge relative reduction algorithms to simplify the input information. Rough...
This paper presents a dynamic mutation particle swarm optimization (DMPSO). The particle swarm optimization (PSO) is a popular swarm algorithm, which has exhibited good performance on many optimization problems. However, similar to other swarm intelligence algorithms, PSO also suffers from premature convergence. Sub swarm and mutation are widely used strategies in the PSO algorithm to overcome the...
The evolutionary neural network is the combination of the evolutionary optimization algorithm and traditional neural network. To overcome the demerits of previously proposed evolutionary neural networks, combining the immune continuous ant colony algorithm proposed by author and BP neural network, a new evolutionary neural network whose architecture and connection weights evolve simultaneously is...
Information entropy is an effective description for the uncertainty of a system, and could be used for the symptom to detect the vibration changes of steam turbine. Based on the faulty signals collected from rotor test rig, three information entropy: singular spectrum entropy, power spectrum entropy, wavelet energy spectrum entropy were calculated as information entropy data. Probability neural networks(PNNs)...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.