Recently, the the support vector machine (SVM) has become a popular tool in time series forecasting. In developing a successful SVM forecaster, the first step is feature extraction. This paper proposes the applications of principal component analysis (PCA), kernel principal component analysis (KPCA) and independent component analysis (ICA) to SVM for feature extraction. The PCA linearly transforms the original inputs into new uncorrelated features. The KPCA is a nonlinear PCA developed by using the kernel method. In ICA, the original inputs are linearly transformed into features which are mutually statistically independent. By examining the sunspot data, Santa Fe data set A and five real futures contracts, the experiment shows that SVM by feature extraction using PCA, KPCA or ICA can perform better than that without feature extraction. Furthermore, among the three methods, there is the best performance in the KPCA feature extraction, followed by the ICA feature extraction.
A new way of identification of minerals was suggested. The identification was based on chemometric analysis of measured IR spectra of selected minerals. IR spectra were collected using diffuse reflectance technique. The discriminant analysis and principal component analysis were used as chemometric methods. Five statistical models were created for separation and identification of clay minerals. Up to 60 samples of various mineral standards (clay minerals, feldspars, carbonates, sulphates and quartz) from different localities were selected for the creation of statistical models. The results of this study confirm that the discriminant analysis of IR spectra of minerals could provide a powerful tool for mineral identification. Even differentiation of muscovite from illite and identification of mixed structures of illite-smectite were achieved., Michal Ritz, Lenka Vaculíková and Eva Plevová., and Obsahuje bibliografii
The type species of the genus Glossocercus Chandler, 1935, G. cyprinodontis Chandler, 1935, was described as metacestode (larval stage) from the mesentery of the sheepshead minnow fish (Cyprinodon variegatus Lacépède) from Galveston Bay, Texas. The description was based on the morphology of the rostellar hooks; however, the features of the internal morphology of the proglottides could no be provided. In the present study we describe for the first time the features of the adult G. cyprinodontis from the intestine of Pelecanus occidentalis Linnaeus, Nycticorax nycticorax Linnaeus and Egretta rufescens Gmelin in Mexico. Glossocercus cyprinodontis possesses similar strobilar morphology with the two other congeneric species, both distributed in the Neartic and Neotropical regions, i.e. Glossocercus caribaensis (Rysavy et Macko, 1971) and Glossocercus auritus (Rudolphi, 1819). However, G. cyprinodontis differs mainly in the shape of the rostellar hooks (those of G. cyprinodontis possess the handle and the guard strongly sclerified compared to those of G. auritus and G. caribaensis) and their size (total length of 175-203 mm in G. cyprinodontis compared to 189-211 mm in G. caribaensis and 220-285 mm in G. auritus). Generic diagnosis of Glossocercus is emended: rostellar hooks in two rows with ten hooks of different shape and length in each, scolex large and globular, proglottides craspedote, wider than long, genital pores irregularly alternating, vagina transverse, surrounded by epithelial cells, ventral to cirrus-sac, uterus bar-shaped in mature proglottides, occupies all space between osmoregulatory ducts with eggs in gravid proglottides, ovary lobed in middle of proglottis, cirrus-sac elongate, between osmoregulatory canals, cirrus armed with spinitriches and apical tuft of slender spinitriches.
Authors compared bird communities living in five mountain areas in the northern Croatia (Risnjak, Papuk, Medvednica, Ivanščica and Cesargrad mountain) using multivariate explorative techniques of qualitative and quantitative historical data. Similarity matrices were prepared based on Bray-Courtis similarity among samples. Non-metric multidimensional scaling (NMDS) and complete linkage clustering on qualitative and quantitative similarity matrix respectively were made. Principal component analysis (PCA) on quantitative data revealed bird species that contributed the most to the variability of samples. First three dimensions explain 75.2% of variance in samples (53.1%, 13.5% and 8.6% respectively) while the greatest loadings are caused by abundant species like Sylvia atricapilla, Erithacus rubecula, Turdus merula and Phylloscopus collybita. Non-metric multidimensional scaling revealed clear pattern in significant similarity among communities at low altitudes and at the same time – insignificant similarity among assemblages at different altitudes above the sea level (exception from the rule applies to the Papuk community at 600 m.a.s.l.). The clustering based on similarity matrix on qualitative data has shown clear separation among communities from different mountain areas. This study suggests that monitoring bird communities in the Croatian mountains must be designed as repeated sampling of quantitative data through time.
Control chart pattern (CCP) recognition is important for monitoring
process environments to achieve appropriate control precisely and quickly and to produce high quality products. CCPs are represented by a large number of inputs. The principal component analysis (PCA) is an effective procedure for reducing a large input vector to a small vector.
This paper describes an efficient approach to reducing the inputs of the networks for CCP recognition with the use of PCA. The reason for applying PCA to CCP recognition is to provide simplicity for the networks and to speed up the training procedure of them. Multilayered perceptrons (MLP) are used and trained with the resilient-propagation (RP) and the backpropagation (BP) learning algorithms. The results show that PCA provides less cornplex neural network structure for accurate and faster training. This helps to achieve the CCP recognition precisely and accurately and rnight even help us to implement the recognition easily within the VLSI technologies for this application.
Control chart patterri (CCP) recognition is important for monitoring
process environments to achieve appropriate control precisely and quickly and to produce high quality products. CCPs are represented by a large number of inputs. The principal component analysis (PCA) is an effective procedure for redncing a large input vector to a small vector.
This paper describes an efficient approach to redncing the inputs of the networks for CCP recognition with the use of PCA. The reason for applying PCA to CCP recognition is to provide simplicity for the networks and to speed up the training procedure of them. Multilayered perceptrons (MLP) are used and trained with the resilient-propagation (RP) and the backpropagation (BP) learning algorithms. The results show that PCA provides less complex neural network structure for accurate and faster training. This helps to achieve the CCP recognition precisely and accurately and rnight even help us to implement the recognition easily within the VLSI technologies for this application.
Monitoring of groundwater quality in Bareilly district, Uttar Pradesh, India, was performed at 10 different sites during the years 2005-2006. Obtained quality parameters were treated using principal component analysis (PCA) and cluster analysis (CA). The study shows usefulness of multivariate statistical techniques for evaluation and interpretation of groundwater quality data sets. and V letech 2005-2006 byla na deseti odběrných místech v regionu Bareilly, Uttar Pradesh, Indie sledována kvalita podzemní vody. Zjištěné kvalitativní parametry byly zpracovány pomocí analýzy hlavního prvku (PCA) a pomocí shlukové analýzy (CA). Studie dokládá vhodnost multivariačních statistických metod pro vyhodnocení a interpretaci změřených výsledků.
Never before in history data has been generated at such high volumes as it is today. It is estimated that every year about 1 Exabyte (= 1 Million Terabyte) of data are generated, of which a large portion is available in digital form. Exploring and analyzing the vast volumes of data becomes increasingly difficult. This paper describes system Vitamin-S that aims to help when analyzing very large data sets.