Structured large margin machines: sensitive to data distributions

Yeung, Daniel; Wang, Defeng; Ng, Wing W.; Tsang, Eric W. K.; Wang, Xizhao

doi:10.1007/s10994-007-5015-9

Cited by 66 publications

(41 citation statements)

References 24 publications

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“…3). That is, RBFNs achieve better overall performance than MLPs for the given spherical-/Gaussiandistributed data [19]. This is well consistent with NFL [20] which states that, for good generalization, there are no context-independent or usage-independent reasons to favor one classification method over another, unless appropriate prior information is incorporated in model selection.…”

Section: Introductionsupporting

confidence: 75%

Plane-Gaussian artificial neural network

Yang

Chen

2011

Neural Comput & Applic

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Multilayer perceptrons (MLPs) and radial basis functions networks (RBFNs) have been widely concerned in recent years. In this paper, based on k-plane clustering (kPC) algorithm, we propose a novel artificial network model termed as Plane-Gaussian network to enlarge the arsenal of the neural networks. This network adopts a so-called Plane-Gaussian activation function (PGF) in hidden neurons. Replacing traditional central point of Gaussian radial basis function (RBF) with central hyperplane, PGF forms a band-shaped rather than spheral-shaped receptive field in RBF, which makes PGF able to express its peculiar geometrical characteristics: locality and globality. Importantly, it is also proved that PGF network (PGFN) having one hidden layer is capable of universal approximation. As a universal approximator, PGFN gives an informal way of bridging the gap between MLP and RBFN. The experiments report comparison between training time and classification accuracies on some artificial and UCI datasets and conclude that (1) PGFN runs significantly faster than MLP and (2) PGFN has comparable or better classification performance than MLP and RBFN, especially in subspace-distributed datasets.

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Section: Introductionsupporting

confidence: 75%

Plane-Gaussian artificial neural network

Yang

Chen

2011

Neural Comput & Applic

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“…The principle of two fork tree-SVM multiple classification method is that there are a training period and a testing period [18]. When training, it starts from the leaf node and then moves towards the root node.…”

Section: ) Two Fork Tree-svm Multiple Classification Methodsmentioning

confidence: 99%

“…In order to observe the energy distribution of language, this paper puts forward the diagram of energy distribution of language [11]. It is able to more intuitively show the…”

Section: ) Energy Distribution Of Languagementioning

confidence: 99%

Experimental Research on Signal Recognition Algorithm of Wireless Sensor Language

Muhaxov¹,

Lou

Li³

et al. 2016

Int. J. Onl. Eng.

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Abstract-In the past several decades, much research has been carried out on the wireless sensor network which is widely used in the fields of national defense and national economy within China. The main function of the language sensor is to transfer the voice signal into an electrical signal so as to facilitate the subsequent analysis and processing. Combined with the wireless network signal, it is widely used in banks, shopping malls, examination rooms, prisons, important places of military affairs and other places. This paper makes a detailed introduction of some theoretical knowledge of language recognition and puts forward the recognition algorithm in which the language signal is abstracted by language features. Afterwards, Matlab is used to make simulation in-depth research and classification based on Support Vector Machine. Finally, a large number of samples are collected for the experiment so as to research the effect of weighted feature value and structure of classification on speech recognition rate. The conclusion of the paper provides a basis for further subsequent theoretical study.

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“…Labeled input-output pairs may be fed to the algorithms during training phase (Stankovic et al 2012;Ganguly 2008;Bishop 2006). The performance of a classifier depends on its ability to classify the unseen data based on the learned model and is more generally known as its generalization ability (Nguyen et al 2008;Yeung et al 2007;Steinwart and Christmann 2008;Tax and Duin 2004). Depending on the type of model learned during the training phase, these techniques can be divided into two types:…”

Section: Classification Based Outlier and Event Detection For Wsns Dementioning

confidence: 99%

One-class support vector machines: analysis of outlier detection for wireless sensor networks in harsh environments

Shahid

Naqvi

Qaisar³

2013

Artif Intell Rev

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Machine learning, like its various applications, has received a great interest in outlier detection in Wireless Sensor Networks. Support Vector Machines (SVM) are a special type of Machine learning techniques which are computationally inexpensive and provide a sparse solution. This work presents a detailed analysis of various formulations of one-class SVMs, like, hyper-plane, hyper-sphere, quarter-sphere and hyper-ellipsoidal. These formulations are used to separate the normal data from anomalous data. Various techniques based on these formulations have been analyzed in terms of a number of characteristics for harsh environments. These characteristics include input data type, spatio-temporal and attribute correlations, user specified thresholds, outlier types, outlier identification(event/error), outlier degree, susceptibility to dynamic topology, non-stationarity and inhomogeneity. A tabular description of improvement and feasibility of various techniques for deployment in the harsh environments has also been presented.

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Structured large margin machines: sensitive to data distributions

Cited by 66 publications

References 24 publications

Plane-Gaussian artificial neural network

Plane-Gaussian artificial neural network

Experimental Research on Signal Recognition Algorithm of Wireless Sensor Language

One-class support vector machines: analysis of outlier detection for wireless sensor networks in harsh environments

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