Improvised Admissible Kernel Function for Support Vector Machines in Banach Space for Multiclass Data

Classification based on supervised learning theory is one of the most significant tasks frequently accomplished by so-called Intelligent Systems. Contrary to the traditional classification techniques that are used to validate or contradict a predefined hypothesis, kernel based classifiers offer the possibility to frame new hypotheses using statistical learning theory (Sangeetha and Kalpana, 2010). Support Vector Machine (SVM) is a standard kernel based learning algorithm where it improves the learning ability through experience. It is highly accurate, robust and optimal kernel based classification technique that is well-suited to many real time applications. In this paper, kernel functions related to Hilbert space and Banach Space are explained. Here, the experimental results are carried out using benchmark multiclass datasets which are taken from UCI Machine Learning Repository and their performance are compared using various metrics like support vector, support vector percentage, training time and accuracy.

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Support Vector Machines and Kernel Functions for Text Processing

Revista de Informática Teórica e Aplicada ◽

10.22456/2175-2745.39702 ◽

2013 ◽

Vol 20 (3) ◽

pp. 130 ◽

Cited By ~ 2

Author(s):

Celso Antonio Alves Kaestner

Keyword(s):

Text Classification ◽

Learning Algorithm ◽

Text Processing ◽

Dimensional Space ◽

Kernel Functions ◽

Support Vector ◽

Svm Classifier ◽

Vector Machines ◽

Automatic Text Classification ◽

Automatic Text

This work presents kernel functions that can be used in conjunction with the Support Vector Machine – SVM – learning algorithm to solve the automatic text classification task. Initially the Vector Space Model for text processing is presented. According to this model text is seen as a set of vectors in a high dimensional space; then extensions and alternative models are derived, and some preprocessing procedures are discussed. The SVM learning algorithm, largely employed for text classification, is outlined: its decision procedure is obtained as a solution of an optimization problem. The “kernel trick”, that allows the algorithm to be applied in non-linearly separable cases, is presented, as well as some kernel functions that are currently used in text applications. Finally some text classification experiments employing the SVM classifier are conducted, in order to illustrate some text preprocessing techniques and the presented kernel functions.

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CLASSIFICATION OF MENTAL TASKS FROM EEG SIGNALS USING EXTREME LEARNING MACHINE

International Journal of Neural Systems ◽

10.1142/s0129065706000482 ◽

2006 ◽

Vol 16 (01) ◽

pp. 29-38 ◽

Cited By ~ 153

Author(s):

NAN-YING LIANG ◽

PARAMASIVAN SARATCHANDRAN ◽

GUANG-BIN HUANG ◽

NARASIMHAN SUNDARARAJAN

Keyword(s):

Extreme Learning Machine ◽

Classification Accuracy ◽

Learning Algorithm ◽

Support Vector ◽

Eeg Signals ◽

Training Time ◽

Vector Machines ◽

Order Of Magnitude ◽

Learning Machine ◽

Mental Tasks

In this paper, a recently developed machine learning algorithm referred to as Extreme Learning Machine (ELM) is used to classify five mental tasks from different subjects using electroencephalogram (EEG) signals available from a well-known database. Performance of ELM is compared in terms of training time and classification accuracy with a Backpropagation Neural Network (BPNN) classifier and also Support Vector Machines (SVMs). For SVMs, the comparisons have been made for both 1-against-1 and 1-against-all methods. Results show that ELM needs an order of magnitude less training time compared with SVMs and two orders of magnitude less compared with BPNN. The classification accuracy of ELM is similar to that of SVMs and BPNN. The study showed that smoothing of the classifiers' outputs can significantly improve their classification accuracies.

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CAFD: Context-Aware Fault Diagnostic Scheme towards Sensor Faults Utilizing Machine Learning

Sensors ◽

10.3390/s21020617 ◽

2021 ◽

Vol 21 (2) ◽

pp. 617

Author(s):

Umer Saeed ◽

Young-Doo Lee ◽

Sana Ullah Jan ◽

Insoo Koo

Keyword(s):

Machine Learning ◽

Learning Algorithm ◽

Diagnostic System ◽

Machine Learning Algorithms ◽

Support Vector ◽

Context Aware ◽

Sensor Faults ◽

Training Time ◽

Low Intensity ◽

Fault Diagnostic

Sensors’ existence as a key component of Cyber-Physical Systems makes it susceptible to failures due to complex environments, low-quality production, and aging. When defective, sensors either stop communicating or convey incorrect information. These unsteady situations threaten the safety, economy, and reliability of a system. The objective of this study is to construct a lightweight machine learning-based fault detection and diagnostic system within the limited energy resources, memory, and computation of a Wireless Sensor Network (WSN). In this paper, a Context-Aware Fault Diagnostic (CAFD) scheme is proposed based on an ensemble learning algorithm called Extra-Trees. To evaluate the performance of the proposed scheme, a realistic WSN scenario composed of humidity and temperature sensor observations is replicated with extreme low-intensity faults. Six commonly occurring types of sensor fault are considered: drift, hard-over/bias, spike, erratic/precision degradation, stuck, and data-loss. The proposed CAFD scheme reveals the ability to accurately detect and diagnose low-intensity sensor faults in a timely manner. Moreover, the efficiency of the Extra-Trees algorithm in terms of diagnostic accuracy, F1-score, ROC-AUC, and training time is demonstrated by comparison with cutting-edge machine learning algorithms: a Support Vector Machine and a Neural Network.

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A Multiple Kernel Learning Model Based on p-Norm

Computational Intelligence and Neuroscience ◽

10.1155/2018/1018789 ◽

2018 ◽

Vol 2018 ◽

pp. 1-7 ◽

Cited By ~ 3

Author(s):

Jinshan Qi ◽

Xun Liang ◽

Rui Xu

Keyword(s):

Multiple Kernel Learning ◽

Research Society ◽

Kernel Functions ◽

Support Vector ◽

Kernel Weights ◽

Multiple Kernel ◽

Vector Machines ◽

Norm Constraint ◽

Special Case ◽

Elastic Constraint

By utilizing kernel functions, support vector machines (SVMs) successfully solve the linearly inseparable problems. Subsequently, its applicable areas have been greatly extended. Using multiple kernels (MKs) to improve the SVM classification accuracy has been a hot topic in the SVM research society for several years. However, most MK learning (MKL) methods employ L1-norm constraint on the kernel combination weights, which forms a sparse yet nonsmooth solution for the kernel weights. Alternatively, the Lp-norm constraint on the kernel weights keeps all information in the base kernels. Nonetheless, the solution of Lp-norm constraint MKL is nonsparse and sensitive to the noise. Recently, some scholars presented an efficient sparse generalized MKL (L1- and L2-norms based GMKL) method, in which L1 L2 established an elastic constraint on the kernel weights. In this paper, we further extend the GMKL to a more generalized MKL method based on the p-norm, by joining L1- and Lp-norms. Consequently, the L1- and L2-norms based GMKL is a special case in our method when p=2. Experiments demonstrated that our L1- and Lp-norms based MKL offers a higher accuracy than the L1- and L2-norms based GMKL in the classification, while keeping the properties of the L1- and L2-norms based on GMKL.

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Model selection for support vector machines: Advantages and disadvantages of the Machine Learning Theory

The 2010 International Joint Conference on Neural Networks (IJCNN) ◽

10.1109/ijcnn.2010.5596450 ◽

2010 ◽

Cited By ~ 19

Author(s):

Davide Anguita ◽

Alessandro Ghio ◽

Noemi Greco ◽

Luca Oneto ◽

Sandro Ridella

Keyword(s):

Machine Learning ◽

Support Vector Machines ◽

Model Selection ◽

Learning Theory ◽

Support Vector ◽

Advantages And Disadvantages ◽

Vector Machines ◽

Selection For

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Efficient Technique for Facial Image Recognition With Support Vector Machines in 2D Images With Cross-validation in Matlab

WSEAS TRANSACTIONS ON SYSTEMS AND CONTROL ◽

10.37394/23203.2020.15.18 ◽

2020 ◽

Vol 15 ◽

Keyword(s):

Support Vector Machines ◽

Cross Validation ◽

Facial Recognition ◽

Kernel Functions ◽

Efficient Technique ◽

Support Vector ◽

Facial Image ◽

Vector Machines ◽

Validation Technique ◽

2D Images

This article presented in the context of 2D global facial recognition, using Gabor Wavelet's feature extraction algorithms, and facial recognition Support Vector Machines (SVM), the latter incorporating the kernel functions: linear, cubic and Gaussian. The models generated by these kernels were validated by the cross validation technique through the Matlab application. The objective is to observe the results of facial recognition in each case. An efficient technique is proposed that includes the mentioned algorithms for a database of 2D images. The technique has been processed in its training and testing phases, for the facial image databases FERET [1] and MUCT [2], and the models generated by the technique allowed to perform the tests, whose results achieved a facial recognition of individuals over 96%.

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Machine vision for field-level wood identification

IAWA Journal ◽

10.1163/22941932-bja10001 ◽

2020 ◽

Vol 41 (4) ◽

pp. 681-698 ◽

Cited By ~ 2

Author(s):

Bruno Geike de Andrade ◽

Vanessa Maria Basso ◽

João Vicente de Figueiredo Latorraca

Keyword(s):

Intelligent Systems ◽

Wood Anatomy ◽

Support Vector ◽

Technical Knowledge ◽

Macroscopic Description ◽

Automated Identification ◽

Vector Machines ◽

Texture Characteristics ◽

Identification Of Species ◽

Occurrence Matrix

Abstract Identifying wood species using wood anatomy is an important tool for various purposes. The traditionally used method is based on the macroscopic description of the physical and anatomical characteristics of the wood. This requires that the identifier has thorough technical knowledge about wood anatomy. A possible alternative to this task is to use intelligent systems capable of identifying species through an analysis of digital images. In this work, 21 species were used to generate a set of 2000 macroscopic images. These were produced with a smartphone under field conditions, from samples manually polished with knives. Texture characteristics obtained through a gray level co-occurrence matrix were used in developing classifiers based on support vector machines. The best model achieved a 97.7% accuracy. Our study concluded that the automated identification of species can be performed in the field in a practical, simple and precise way.

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Classification in GIS Using Support Vector Machines

Handbook of Research on Geoinformatics ◽

10.4018/978-1-59140-995-3.ch014 ◽

2010 ◽

pp. 106-112 ◽

Cited By ~ 1

Author(s):

Alina Lazar ◽

Bradley A. Shellito

Keyword(s):

Support Vector Machines ◽

Urban Development ◽

Rural Areas ◽

Urban Areas ◽

Kernel Functions ◽

Support Vector ◽

Future Trends ◽

Error Signal ◽

Vector Machines

Support Vector Machines (SVM) are powerful tools for classification of data. This article describes the functionality of SVM including their design and operation. SVM have been shown to provide high classification accuracies and have good generalization capabilities. SVM can classify linearly separable data as well as nonlinearly separable data through the use of the kernel function. The advantages of using SVM are discussed along with the standard types of kernel functions. Furthermore, the effectiveness of applying SVM to large, spatial datasets derived from Geographic Information Systems (GIS) is also described. Future trends and applications are also discussed – the described extracted dataset contains seven independent variables related to urban development plus a class label which denotes the urban areas versus the rural areas. This large dataset, with over a million instances really proves the generalization capabilities of the SVM methods. Also, the spatial property allows experts to analyze the error signal.

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Research of a New SVM Kernel Function

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.543-547.1659 ◽

2014 ◽

Vol 543-547 ◽

pp. 1659-1662

Author(s):

Juan Du ◽

Wen Long Zhang ◽

Meng Meng Xie

Keyword(s):

Kernel Function ◽

Homogeneous Polynomial ◽

Information Filtering ◽

Kernel Functions ◽

Learning Ability ◽

New Combination ◽

Polynomial Kernel ◽

Support Vector ◽

Text Information ◽

Information Recognition

The kernel was the key technology of SVM; the kernel affected the learning ability and generalization ability of support vector machine. Aiming at the specific application of harmful text information recognition, combining traditional kernel function the paper structured a new combination kernel, modeling for the independent harmful vocabulary and co-occur vocabularies, and then evaluation the linear kernel, homogeneous polynomial kernel, non homogeneous polynomial kernel and combination kernel function in the sample experiment. The experimental results of combination kernel function showed that the effect has increased greatly than other kernel functions for the application of harmful text information filtering. Especially the Rcall value achieved satisfactory results.

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