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Auto tree classification is of use in plant research. We put forward and implement a hibernal tree automatic classification system. In the paper features of tree system and other aspects that could influence classification results are analyzed and taken into consideration in classifier modeling. We extract the most related contents and information for classification, set up hibernal trees classification...
Feature selection is among the keys in many applications, especially in mining high-dimensional data. With lack of labeled instances, the learning accuracy may deteriorate using traditional methods. In this paper, we introduce a ldquowrapperrdquo type semi-supervised feature selection approach based on RSC model. It extends the class label from labeled training set to unlabeled data. Additionally,...
The problem of ranking has recently gained attention in data learning. The goal ranking is to learn a real-valued ranking function that induces a ranking or ordering over an instance space. In this paper, we apply popular Bayesian techniques on ranking support vector machine. We propose a novel differentiable loss function called trigonometric loss function with the desirable characteristic of natural...
A number of recent works have proposed using data mining and machine learning techniques to classify traffic flows based on statistical flow characteristics. Most of these classifiers work offline, since full-flow statistics are not available until a flow is finished. Therefore, it is usually too late to take actions for online deployment. In this paper, we propose a simple and effective technique...
Ant colony optimization (ACO) is a kind of bionic swarm intelligence algorithm belongs to artificial intelligence (AI) field and has been successfully applied in resolving complex optimization problems. Support vector machine (SVM) is a new machine learning method with greater generalization performance, and has shown its superiority in classification and regression problems. By combining the advantages...
Neural network techniques have been widely applied to areas of such as data mining, information integration and grid computing. This paper proposes a new learning algorithm based on trust region optimization theory. In the paper, the Dogleg-algorithm to obtain the valid trust region steps is presented, and a self-adjustable method with variable coefficients is given to resolve the problem of oscillatory...
This paper proposed a hybrid evolutionary system HPSONN to automatically design artificial neural networks (ANNpsilas), where ANNpsilas structure and parameters are tuned simultaneously. In HPSONN, an improved particle swarm optimization using optimal foraging theory (PSOOFT) and a binary particle swarm optimization (BPSO) are used to train ANNpsilas free parameters (weights and bias) and find optimal...
This paper introduces multi-output support vector machine regression (M-SVR) by using the re-weight iterative algorithm. Then the problem of online learning of M-SVR is solved by given the iterative formula for the weight of regression function using the gradient descent algorithm of instantaneous risk. Computer experiments show that the accuracy and workload of the algorithm are superior to that...
Actually, various kinds of sources (such as voice, video or data) with diverse traffic characteristics and quality of service requirements (QoS), which are multiplexed at very high rates, leads to significant traffic problems such as packet losses, transmission delays, delay variations, etc, caused mainly by congestion in the networks. The prediction of these problems in real time is quite difficult,...
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