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Our results obtained from the gradient boosting and neural network methods outperformed the state-of-the-art classification approaches.
A couple of state of the art classification methods are integrated to classify the braking states of the Segway.
The performance of our algorithm is studied experimentally on 19 publicly available data sets and further compared to several state-of-the-art classification approaches.
There exist numerous state of the art classification algorithms that are designed to handle the data with nominal or binary class labels.
This paper presents and discusses an evolutionary based adaptive drift-correction method designed to work with state-of-the-art classification systems.
Diagnosis schemes are applied using various state-of-the-art classification algorithms and the results are computed based on accuracy, sensitivity, specificity, and F-measure.
We show that the proposed structure provides efficient coverage of the decision space which leads to state-of-the art classification accuracy and fast training times.
Support vector machine is a unique state-of-the-art classification and regression technique based on the framework of Vapnik's statistical learning theory (Cortes and Vapnik 1995) designed to solve complex regression problems.
In order to deal with the large set of training data, the method of Random Forests was employed; this is a state-of-the-art classification algorithm in the field of computer vision.
While most of the state-of-the-art classification approaches need input data to have the same size, the LNBNN classifier permits classifying data with different sizes of the feature vector.
Jochem et al., 2009, Heinzel and Koch, 2011, Mongus and Zalik, 2012 presented methodology for the pre-processing of the LiDAR data using State of the Art classification methods.
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