Interpretation of Handwritten Single-Stroke Graffiti Using Support Vector Machines

Lam, Hak‐Keung; Prada, J.

doi:10.1142/s1469026809002655

Cited by 5 publications

(4 citation statements)

References 19 publications

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“…The Support Vector Machine (SVM) was first proposed by Vapnik in 1995 [7] as a machine learning model that which can be applied to various supervised and unsupervised learning applications [12]- [14].…”

Section: Introductionmentioning

confidence: 99%

Classification of epilepsy seizure phase using interval type-2 fuzzy support vector machines

et al. 2016

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Section: Introductionmentioning

confidence: 99%

Classification of epilepsy seizure phase using interval type-2 fuzzy support vector machines

et al. 2016

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“…Classification is a process that takes samples from objects and assigns each one of them to a pre-defined group or class label. This is a promising and important field of research which provides a solution to a wide range of applications e.g., classification of different investments and lending opportunities as acceptable or unacceptable risk [1], classification of electrocardiogram (ECG) arrhythmias [2], classification of ECG beat [3], facial recognition [4,5], hand-writing recognition [6][7][8][9][10], heart sound classification [11], human body posture classification [12], speaker verification [13], speech recognition [14,15] and text classification [16,17].…”

Section: Introductionmentioning

confidence: 99%

“…An example of traditional methods is the cover linear discriminant analysis [18], logic based method (e.g., decision trees [19]), statistical approach (e.g., Bayesian classification [20]) and instance-based methods (e.g., nearest neighbour algorithm [21,22]). Machine learning methods include the support vector machines and neural network (NN) [4,9,13,17].…”

Section: Introductionmentioning

confidence: 99%

Variable weight neural networks and their applications on material surface and epilepsy seizure phase classifications

et al. 2015

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This paper presents a novel neural network having variable weights, which is able to improve its learning and generalisation capabilities, to deal with classification problems. The variable weight neural network (VWNN) allows its weights to be changed in operation according to the characteristic of the network inputs so that it can adapt to different characteristics of input data resulting in better performance compared with ordinary neural networks with fixed weights. The effectiveness of the VWNN is tested with the consideration of two real-life applications. The first application is on the classification of materials using the data collected by a robot finger with tactile sensors sliding along the surface of a given material. The second application considers the classification of seizure phases of epilepsy (seizure-free, pre-seizure and seizure phases) using real clinical data. Comparisons are performed with some traditional classification methods including neural network, k-nearest neighbours and naive Bayes classification techniques. It is shown that the VWNN classifier outperforms the traditional methods in terms of classification accuracy and robustness property when the input data is contaminated with noise.

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“…In view of the superior learning and generalization capability of the neural networks, we are motivated to implement classifiers using neural networks to deal with the material classification problem [32]- [34]. In this study, the characteristics of the neural networks are considered for the implementation of neural-network-based classifiers, demonstrating different levels of flexibility, scalability and complexity.…”

mentioning

confidence: 99%

A study of neural-network-based classifiers for material classification

Lam¹,

Ekong²,

Liu³

et al. 2014

Neurocomputing

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In this paper, the performance of the commonly used neural-network-based classifiers is investigated on solving a classification problem which aims to identify the object nature based on surface features of the object.When the surface data is obtained, a proposed feature extraction method is used to extract the surface feature of the object. The extracted features are then used as the inputs for the classifier. This research studies eighteen household objects which are requisite to our daily life. Six commonly used neural-network-based classifiers, namely one-against-all, weighted one-against-all, binary coded, parallel-structured, weighted parallel structured and tree-structured, are investigated. The performance for the six neural-network-based classifiers is evaluated based on recognition accuracy for individual object. Also, two traditional classifiers, namely k-nearest neighbor classifier and naive Bayes classifier, are employed for the comparison purposes. To evaluate robustness property of the classifiers, the original clean data is contaminated with Gaussian white noise. Experimental results show that the parallel-structured, tree-structured and the naive Bayes classifiers outperform the others under the noise-free data. The tree-structured classifier demonstrates the best robustness property under the noisy data.

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Interpretation of Handwritten Single-Stroke Graffiti Using Support Vector Machines

Cited by 5 publications

References 19 publications

Classification of epilepsy seizure phase using interval type-2 fuzzy support vector machines

Classification of epilepsy seizure phase using interval type-2 fuzzy support vector machines

Variable weight neural networks and their applications on material surface and epilepsy seizure phase classifications

A study of neural-network-based classifiers for material classification

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