Temat: Support Vector Machine (SVM)

Skocz do pozycji: 1.

Tytuł:: How To Construct Support Vector Machines Without Breaching Privacy
Autorzy:: Zhan, J.
Chang, L.
Matwin, S.
Tematy:: privacy
security
support vector machine (SVM); Pokaż więcej
Wydawca:: Uniwersytet Przyrodniczo-Humanistyczny w Siedlcach
Powiązania:: https://bibliotekanauki.pl/articles/92993.pdf Link otwiera się w nowym oknie
Opis:: This paper addresses the problem of data sharing among multiple parties in the following scenario: without disclosing their private data to each other, multiple parties, each having a private data set, want to collaboratively construct support vector machines using a linear, polynomial or sigmoid kernel function. To tackle this problem, we develop a secure protocol for multiple parties to conduct the desired computation. In our solution, multiple parties use homomorphic encryption and digital envelope techniques to exchange the data while keeping it private. All the parties are treated symmetrically: they all participate in the encryption and in the computation involved in learning support vector machines.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Evaluation of electromagnetic filtration efficiency using least squares support vector model
Autorzy:: Yuceer, M.
Yildiz, Z.
Abbasov, T.
Tematy:: electromagnetic filtration
disperse systems
support vector machine (SVM); Pokaż więcej
Wydawca:: Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Powiązania:: https://bibliotekanauki.pl/articles/110758.pdf Link otwiera się w nowym oknie
Opis:: The present study aims to apply a least squares support vector model (LS–SVM) for predicting cleaning efficiency of an electromagnetic filtration process, also called quality factor, in order to remove corrosion particles (rust) of low concentrations from water media. For this purpose, three statistical parameters: correlation coefficient, root mean squared error and mean absolute percentage error were calculated for evaluating the performance of the LS–SVM model. It was found that the developed LS–SVM can be used to predict the effectiveness of electromagnetic filtration process.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

Tytuł:: Analysis of new method of initialisation of neuro - fuzzy systems with support vector machines
Analiza nowej metody inicjalizacji systemów neuronowo – rozmytych z wykorzystaniem maszyn wektorów wspierających
Autorzy:: Simiński, K.
Tematy:: support vector machine (SVM)
neuro-fuzzy systems
classification
regression; Pokaż więcej
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Powiązania:: https://bibliotekanauki.pl/articles/375675.pdf Link otwiera się w nowym oknie
Opis:: The correspondence between support vector machines and neuro-fuzzy systems is very interesting. The full equivalence for classification and partial for regression has been formally shown. The equivalence has very interesting implication. It is a base for a new method of initialization of neurofuzzy systems, ie. for creating of fuzzy rule base. The commonly used methods are based on reversion of item: the premises of fuzzy rules split input domain into region, thus premises of fuzzy rules can be elaborated by partition of input domain. This leads to three main classes of partition of input domain. The above mentioned equivalence results in new way of creating the rule base. Now the input domain is not partitioned, but the premises of fuzzy rules are extracted from support vector. The objective of the paper is to examine the advantages and disadvantages of this new method for creation of fuzzy rule bases for neuro-fuzzy systems.
Związek pomiedzy maszynami wektorów podpierajacych i systemami neuronoworozmytymi jest bardzo interesujący. Została wykazana pełna odpowiedniość między tymi systemami dla klasyfikacji i częściowa dla regresji. Odpowiedność ta ma bardzo ważną konsekwencję. Jest podstawa do opracowania nowego sposobu tworzenia bazy reguł dla systemu neuronowo-rozmytego. Dotychczasowe metody opieraja się na podziale przestrzeni wejściowej, a następnie przekształcenia tak powstałych regionów w przesłanki rozmytych reguł. Tutaj możliwe jest przekształcanie wektorów wspierających na przesłanki reguł rozmytych. Celem artykułu jest przebadanie możliwości stosowania takiego podejścia do inicjalizacji systemów neuronowo-rozmytych. Eksperymenty wykazują dosć istotną wadę tego podejścia. W jego wyniku powstają bardzo liczne zbiory reguł rozmytych, co zupełnie przeczy idei interpretowalności wiedzy w systemach neuronowo-rozmytych. Manipulacja pewnymi parametrami umożliwia zmiejszenie liczby reguł, jednak manipulacja ta jest trudna i wymaga wielu prób. Drugą dość istotna wadą jest wyraźnie wyższy błąd wypracowywany przez systemy inicjalizowane przez SVM w porównaniu do systemów, których bazy reguł tworzone sa˛ poprzez podział przestrzeni wejściowej.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 4.

Tytuł:: Implementation of Bilinear Separation algorithm as a classification method for SSVEP-based brain-computer interface
Autorzy:: Jukiewicz, M.
Cysewska-Sobusiak, A.
Tematy:: brain-computer interface
SSVEP
bilinear separation
support vector machine (SVM); Pokaż więcej
Wydawca:: Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Powiązania:: https://bibliotekanauki.pl/articles/114357.pdf Link otwiera się w nowym oknie
Opis:: : The aim of this study was to create a two-class brain-computer interface. As in the case of research on SSVEP stimuli flashing at different frequencies were presented to four subjects. Optimal SSVEP recognition results can be obtained from electrodes: O1, O2 and Oz. In this work SVM classifier with Bilinear Separation algorithm have been compared. The best result in the offline tests using Bilinear Separation was: average accuracy of stimuli recognition 93% and ITR 33.1 bit/min, SVM: 90% and 32.8 bit/min.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 5.

Tytuł:: Fast near-infrared palmprint recognition using nonnegative matrix factorization extreme learning machine
Autorzy:: Xu, X.
Zhang, X.
Lu, L.
Deng, W.
Zuo, K
Tematy:: extreme learning machine
palmprint recognition
superior speed
support vector machine (SVM); Pokaż więcej
Wydawca:: Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Powiązania:: https://bibliotekanauki.pl/articles/173572.pdf Link otwiera się w nowym oknie
Opis:: Support vector machine and artificial neural network are widely used in classification applications. Extreme learning machine (ELM) is a novel and efficient learning algorithm based on the generalized single hidden layer feed forward networks, which performs well in classification applications. The research results have shown the superiority of ELM with the existing classical algorithms: support vector machine (SVM) and back propagation neural network. In this study, we firstly propose a novel nonnegative matrix factorization extreme learning machine (NMFELM) to improve the performance of standard ELM method. Then we propose a novel near-infrared palmprint recognition approach based on NMFELM classifier. As the test data, we use the near-infrared palmprint database provided by Hong Kong Polytechnic University. The experimental results demonstrate that the proposed NMFELM method outperforms the standard ELM- and SVM-based methods.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 6.

Tytuł:: Faults Classification Of Power Electronic Circuits Based On A Support Vector Data Description Method
Autorzy:: Cui, J.
Tematy:: power electronic circuits
fault classification
support vector data description
support vector machine (SVM); Pokaż więcej
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Powiązania:: https://bibliotekanauki.pl/articles/220938.pdf Link otwiera się w nowym oknie
Opis:: Power electronic circuits (PECs) are prone to various failures, whose classification is of paramount importance. This paper presents a data-driven based fault diagnosis technique, which employs a support vector data description (SVDD) method to perform fault classification of PECs. In the presented method, fault signals (e.g. currents, voltages, etc.) are collected from accessible nodes of circuits, and then signal processing techniques (e.g. Fourier analysis, wavelet transform, etc.) are adopted to extract feature samples, which are subsequently used to perform offline machine learning. Finally, the SVDD classifier is used to implement fault classification task. However, in some cases, the conventional SVDD cannot achieve good classification performance, because this classifier may generate some so-called refusal areas (RAs), and in our design these RAs are resolved with the one-against-one support vector machine (SVM) classifier. The obtained experiment results from simulated and actual circuits demonstrate that the improved SVDD has a classification performance close to the conventional one-against-one SVM, and can be applied to fault classification of PECs in practice.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 7.

Tytuł:: Soft Sensing Method Of LS-SVM Using Temperature Time Series For Gas Flow Measurements
Autorzy:: Xu, W.
Fan, Z.
Cai, M.
Shi, Y.
Tong, X.
Sun, J.
Tematy:: gas flow
soft sensor
support vector machine (SVM)
temperature time series; Pokaż więcej
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Powiązania:: https://bibliotekanauki.pl/articles/221824.pdf Link otwiera się w nowym oknie
Opis:: This paper proposes a soft sensing method of least squares support vector machine (LS-SVM) using temperature time series for gas flow measurements. A heater unit has been installed on the external wall of a pipeline to generate heat pulses. Dynamic temperature signals have been collected upstream of the heater unit. The temperature time series are the main secondary variables of soft sensing technique for estimating the flow rate. A LS-SVM model is proposed to construct a non-linear relation between the flow rate and temperature time series. To select its inputs, parameters of the measurement system are divided into three categories: blind, invalid and secondary variables. Then the kernel function parameters are optimized to improve estimation accuracy. The experiments have been conducted both in the single-pulse and multiple-pulse heating modes. The results show that estimations are acceptable.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 8.

Tytuł:: A linear Support Vector Machine solver for a large number of training examples
Autorzy:: Białoń, P.
Tematy:: support vector machine (SVM)
analytic center cutting plane method
RAM volume required; Pokaż więcej
Wydawca:: Polska Akademia Nauk. Instytut Badań Systemowych PAN
Powiązania:: https://bibliotekanauki.pl/articles/970794.pdf Link otwiera się w nowym oknie
Opis:: A new linear Support Vector Machine algorithm and solver are presented. The algorithm is in a twofold way well-suited for problems with a large number of training examples. First, unlike many optimization algorithms, it does not simultaneously keep all the examples in RAM and thus does not exhaust the memory (moreover, it smartly passes through disk files storing the data: two mechanisms reduce the computation time by disregarding some input data without a loss in solution quality). Second, it uses the analytical center cutting plane scheme, appearing as more efficient for hard parameter settings than the Kelley's scheme used in other solvers, like SVM_perf. The experiments with both real-life and artificial examples are described. In one of them the solver proved to be capable of solving a problem with one billion training examples. A critical analysis of the complexity of SVM_perf is given.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 9.

Tytuł:: Fall Detector Using Discrete Wavelet Decomposition And SVM Classifier
Autorzy:: Wójtowicz, B.
Dobrowolski, A.
Tomczykiewicz, K.
Tematy:: fall detection
discrete wavelet transform
data fusion
support vector machine (SVM); Pokaż więcej
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Powiązania:: https://bibliotekanauki.pl/articles/220495.pdf Link otwiera się w nowym oknie
Opis:: This paper presents the design process and the results of a novel fall detector designed and constructed at the Faculty of Electronics, Military University of Technology. High sensitivity and low false alarm rates were achieved by using four independent sensors of varying physical quantities and sophisticated methods of signal processing and data mining. The manuscript discusses the study background, hardware development, alternative algorithms used for the sensor data processing and fusion for identification of the most efficient solution and the final results from testing the Android application on smartphone. The test was performed in four 6-h sessions (two sessions with female participants at the age of 28 years, one session with male participants aged 28 years and one involving a man at the age of 49 years) and showed correct detection of all 40 simulated falls with only three false alarms. Our results confirmed the sensitivity of the proposed algorithm to be 100% with a nominal false alarm rate (one false alarm per 8 h).
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 10.

Tytuł:: A real-valued genetic algorithm to optimize the parameters of support vector machine for classification of multiple faults in NPP
Autorzy:: Amer, F. Z.
El-Garhy, A. M.
Awadalla, M. H.
Rashad, S. M.
Abdien, A. K.
Tematy:: support vector machine (SVM)
fault classification
multi fault classification
genetic algorithm (GA)
machine learning; Pokaż więcej
Wydawca:: Instytut Chemii i Techniki Jądrowej
Powiązania:: https://bibliotekanauki.pl/articles/147652.pdf Link otwiera się w nowym oknie
Opis:: Two parameters, regularization parameter c, which determines the trade off cost between minimizing the training error and minimizing the complexity of the model and parameter sigma (σ) of the kernel function which defines the non-linear mapping from the input space to some high-dimensional feature space, which constructs a non-linear decision hyper surface in an input space, must be carefully predetermined in establishing an efficient support vector machine (SVM) model. Therefore, the purpose of this study is to develop a genetic-based SVM (GASVM) model that can automatically determine the optimal parameters, c and sigma, of SVM with the highest predictive accuracy and generalization ability simultaneously. The GASVM scheme is applied on observed monitored data of a pressurized water reactor nuclear power plant (PWRNPP) to classify its associated faults. Compared to the standard SVM model, simulation of GASVM indicates its superiority when applied on the dataset with unbalanced classes. GASVM scheme can gain higher classification with accurate and faster learning speed.
Dostawca treści:: Biblioteka Nauki

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