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Real-time crash prediction models are playing a key role in transportation information system. Support vector machine (SVM), a classification learning algorithm, was introduced to evaluate real-time crash risk. The size of traffic dataset is always large with a high accumulating speed. By applying a warm start strategy, an incremental learning algorithm is introduced to update the original model....
For traditional data mining tasks, algorithms are commonly selected by manual effort. However, it is a challenge for any practitioner to select the most appropriate algorithm from hundreds of candidates. To address this issue, we have proposed a novel model for supporting automatic selection on data mining algorithms. The model incorporates the extracted characteristics of data sets and the dynamically...
Recently, sparse approximation has become a preferred method for learning large scale kernel machines. This technique attempts to represent the solution with only a subset of original data points also known as basis vectors, which are usually chosen one by one with a forward selection procedure based on some selection criteria. The computational complexity of several resultant algorithms scales as...
This paper proposes a general boosting framework for combining multiple kernel models in the context of both classification and regression problems. Our main approach is built on the idea of gradient boosting together with a new regularization scheme and aims at reducing the cubic complexity of training kernel models. We focus mainly on using the proposed boosting framework to combine kernel ridge...
Support vector machines (SVMs) have been very successful in pattern recognition and function estimation problems, but in the support vector machines for classification, the training examples are non-fuzzy input and output is y=plusmn1;. In this paper, we introduce the support vector machine in which the training examples are fuzzy input, and give some solving procedure of the support vector machine...
Sensor fusion is a method of integrating signals from multiple sources. This paper investigated the possibility of using a new universal approximator: support vector machines (SVMs), as the sensor fusion architecture for the accuracy measurement and estimation of lumber moisture content in the wood drying process. The result of comparative analysis with multilayer perceptron was given. The training...
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