Gaussian kernel

This paper introduces a novel knowledge based neural network models that incorporate and adapt both existing logistic regression formulas and kernel functions in there structures to improve the learning and adaptation ability of a connectionist model when there is an existing knowledge on the problem in the form of a logistic regression. Different from standard feed-forward neural networks, the proposed

Organic soils represent a substantial pool of carbon in Denmark. The need for carbon stock assessment calls for more rapid and effective mapping methods to be developed. The aim of this study was to compare traditional soil mapping with maps produced from the results of a mobile VIS/NIR system and to evaluate the ability to estimate TOC and map the area of organic soils. The Veris mobile VIS/NIR spectroscopy system was compared to traditional manual sampling. The system is developed for in-situ near surface measurements of soil carbon content. It measures diffuse reflectance in the 350 nm-2200 nm region. The system consists of two spectrophotometers mounted on a toolbar and pulled by a tractor. Optical measurements are made through a sapphire window at the bottom of the shank. The shank was pulled at a depth of 5-7 cm at a speed of 4-5 km/hr. 20-25 spectra per second with 8 nm resolution were acquired by the spectrometers. Measurements were made on 10-12 m spaced transects. The syst...

This paper presents a new approach to the feature extraction for reliable heart rhythm recognition. This system of classification is comprised of three components including data preprocessing, feature extraction and classification of ECG signals. Two different feature extraction methods are applied together to obtain the feature vector of ECG data. The wavelet transform is used to extract the coefficients of the transform as the features of each ECG segment. Simultaneously, autoregressive modelling (AR) is also applied to obtain the temporal structures of ECG waveforms. Then the support vector machine (SVM) with Gaussian kernel is used to classify different ECG heart rhythm. Computer simulations are provided to verify the performance of the proposed method. From computer simulations, the overall accuracy of classification for recognition of 6 heart rhythm types reaches 99.68%

Case study addresses NW slopes of Frus¿ka GoraMountain, Serbia. Landslide activity is quite notorious in this region, especially along the Danube's right river bank, and recently intensified seismicity coupled with atmospheric precipitation might be critical for triggering new landslide occurrences. Hence, it is not a moment too soon for serious landslide susceptibility assessment in this region. State-of-the-art approaches had been taken into consideration, cutting down to the support vector machine (SVM) and k-nearest neighbor (k-NN) algorithms, trained upon expert based model of landslide susceptibility (a multi-criteria analysis). The latter involved Analytical Hierarchy Process (AHP) for weighting influences of different input parameters. These included elevation, slope angle, aspect, distance from flows, vegetation cover, lithology, and rainfall, to represent the natural factors of the slope stability. Processed in a GIS environment (as discrete or float raster layers) trough AHP, those parameters yielded susceptibility pattern, classified by the entropy model into four classes. Subsequently the susceptibility pattern has been featured as training set in SVM and k-NN algorithms. Detailed fitting involved several cases, among which SVM with Gaussian kernel over geo-dataset (coordinates and input parameters) reached the highest accuracy (88%)outperforming other considered cases by far.

A new method of bandwidth selection for kernel density estimators is proposed. The method, termed indirect cross-validation, or ICV, makes use of so-called selection kernels. Least squares cross-validation (LSCV) is used to select the bandwidth of a selection-kernel estimator, and this bandwidth is appropriately rescaled for use in a Gaussian kernel estimator. The proposed selection kernels are linear combinations of

It is well known that nonlinear approximation has an advantage over linear schemes in the sense that it provides comparable approximation rates to those of the linear schemes, but to a larger class of approximands. This was established for spline approximations and for wavelet approximations, and more recently by DeVore and Ron (in press) [2] for homogeneous radial basis function (surface spline) approximations. However, no such results are known for the Gaussian function, the preferred kernel in machine learning and several engineering problems. We introduce and analyze in this paper a new algorithm for approximating functions using translates of Gaussian functions with varying tension parameters. At heart it employs the strategy for nonlinear approximation of DeVore–Ron, but it selects kernels by a method that is not straightforward. The crux of the difficulty lies in the necessity to vary the tension parameter in the Gaussian function spatially according to local information abou...

Support vector machine (SVM) model is employed and tested for the soil surface roughness classification. SVM is calibrated (trained) and tested with the experimentally obtained data. The experimentally data is obtained by using X-band (9.5 GHz) scatterometer for two soil surface roughness 3.78 cm and 1.83 cm at constant soil surface moisture equal to 22.80%. The measurement of the scattering coefficient was carried out over a range of incidence angle from 20° to 70° at the step of 5° for both the HH and vv polarization. The performance of the SVM model is evaluated from the outcome classification result on trained data set and test data set. Radial Gaussian kernel function results 100% correct Classification and identification of soil surface roughness both in training and validation phase. SVM is a proficient technique for soil surface roughness classification by such experimentation and have numerous of advantages over artificial neural network (ANN) based approaches and other theoretical approaches as its less complexity and less time consumption ability.

Standard SVM training has O(m3) time and O(m2) space complexities, where m is the training set size. It is thus computationally infeasible on very large data sets. By observing that practical SVM implementations only approximate the optimal solution by an iterative strategy, we ...

Majority of the videos that have been captured by mobile cameras are suffering from low quality due to either low end manufacturing designs or complicated operating environments and untrained users. Thus videos taken by hand held mobile cameras tend to suffer from different undesired slow motions that cause annoying shaky motion and jitter. It is desirable to stabilize the video sequence by removing the undesired motion between the successive frames. Current methods are applicable to only specific camera motion models; hence having limitation to process gorse motion. In this paper an efficient video stabilization algorithm for hand held camera videos has been proposed. The proposed algorithm uses differential global motion estimation with Taylor series expansion to improve the estimation efficiency. Affine motion model has been assumed to define the inter-frame error between consecutive frames. Motion vectors have been estimated analytically by solving the derivatives of the inter-f...

Screening mammography has been established worldwide for early detection of breast cancer, one of the main causes of death among women in occidental countries. In this paper, we aim at moving towards computer-aided diagnostics of screening mammography. Tissue and lesion are classified using the methodology of content-based image retrieval. In addition, we aim at comprehensive evaluation and have established a large database of annotated reference images (ground truth), which has been merged and unified from different sources publicly available to research. In total, 10,509 mammographic images have been collected from the different sources. From this, 3,375 images are provided with one and 430 radiographs with more than one chain code annotations. This data supports experiments with up to 12 classes, and 233 images per class if a equal distribution is required. Using a two-dimensional principal component analysis with four eigenvalues and a support vector machine with Gaussian kernel for feature extraction and image retrieval, respectively, the precision of computer-aided diagnosis is above 80%. It therefore may be used as second opinion in screening mammography.