On Performing Classification Using SVM with Radial Basis and Polynomial Kernel Functions

Prajapati, Gend Lal; Patle, Arti

doi:10.1109/icetet.2010.134

Cited by 59 publications

(31 citation statements)

References 9 publications

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“…In this table, we compared the best results of our proposed method with the state-of-art method Support Vector Machines. To perform this comparison, we performed the experiments using two approaches: the linear SVM (SVM-L) and polynomial SVM (SVM-P) [8], [9]. From the curves presented at Figures 4 to 9, we can conclude that increasing the quantity of principal components used improves the system performance.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Influence of Quantity of Principal Component in Discriminative Filtering

Santos¹,

Silva²,

Waldir³

2014

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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Discriminative filtering is a pattern recognition technique which aim maximize the energy of output signal when a pattern is found. Looking improve the performance of filter response, was incorporated the principal component analysis in discriminative filters design. In this work, we investigate the influence of the quantity of principal components in the performance of discriminative filtering applied to a facial fiducial point detection system. We show that quantity of principal components directly affects the performance of the system, both in relation of true and false positives rate.

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Section: Experiments and Resultsmentioning

confidence: 99%

Influence of Quantity of Principal Component in Discriminative Filtering

Santos¹,

Silva²,

Waldir³

2014

Computer Science &Amp; Information Technology ( CS &Amp; IT )

View full text Add to dashboard Cite

show abstract

“…Support vector machines allow flexible selection of the degree to which one wishes to have a wide plane of separation versus the number of points that are wrong owing to the wide plane. These learning machines were invented some time ago (42), and the reason for their recent greater popularity is the addition of basis functions that can map points to other dimensions by using nonlinear relationships (43,44) and thus classify examples that are not linearly separable. This capability gives support vector machine algorithms a big advantage over many other machine learning methods.…”

Section: Types Of Machine Learning Algorithmsmentioning

confidence: 99%

Machine Learning for Medical Imaging

et al. 2017

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Machine learning is a technique for recognizing patterns that can be applied to medical images. Although it is a powerful tool that can help in rendering medical diagnoses, it can be misapplied. Machine learning typically begins with the machine learning algorithm system computing the image features that are believed to be of importance in making the prediction or diagnosis of interest. The machine learning algorithm system then identifies the best combination of these image features for classifying the image or computing some metric for the given image region. There are several methods that can be used, each with different strengths and weaknesses. There are open-source versions of most of these machine learning methods that make them easy to try and apply to images. Several metrics for measuring the performance of an algorithm exist; however, one must be aware of the possible associated pitfalls that can result in misleading metrics. More recently, deep learning has started to be used; this method has the benefit that it does not require image feature identification and calculation as a first step; rather, features are identified as part of the learning process. Machine learning has been used in medical imaging and will have a greater influence in the future. Those working in medical imaging must be aware of how machine learning works.

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“…This paper uses Radial basis function (RBF) kernel in SVM, which is viable for leukocyte classi¯cation because of its high accuracy. 32 The function is de¯ned as…”

Section: Classi¯cationmentioning

confidence: 99%

Spatial-spectral identification of abnormal leukocytes based on microscopic hyperspectral imaging technology

Zhou

et al. 2020

J. Innov. Opt. Health Sci.

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Screening and diagnosing of abnormal Leukocytes are crucial for the diagnosis of immune diseases and Acute Lymphoblastic Leukemia (ALL). As the deterioration of abnormal leukocytes is mainly due to the changes in the chromatin distribution, which signi¯cantly a®ects the absorption and re°ection of light, the spectral feature is proved to be important for leukocytes classi¯cation and identi¯cation. This paper proposes an accurate identi¯cation method for healthy and abnormal leukocytes based on microscopic hyperspectral imaging (HSI) technology which combines the spectral information. The segmentation of nucleus and cytoplasm is obtained by the morphological watershed algorithm. Then, the spectral features are extracted and combined with the spatial features. Based on this, the support vector machine (SVM) is applied for classi¯cation of¯ve types of leukocytes and abnormal leukocytes. Compared with di®erent classi¯cation methods, the proposed method utilizes spectral features which highlight the differences between healthy leukocytes and abnormal leukocytes, improving the accuracy in the classi¯cation and identi¯cation of leukocytes. This paper only selects one subtype of ALL for test, and the proposed method can be applied for detection of other leukemia in the future.

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On Performing Classification Using SVM with Radial Basis and Polynomial Kernel Functions

Cited by 59 publications

References 9 publications

Influence of Quantity of Principal Component in Discriminative Filtering

Influence of Quantity of Principal Component in Discriminative Filtering

Machine Learning for Medical Imaging

Spatial-spectral identification of abnormal leukocytes based on microscopic hyperspectral imaging technology

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