Search results for "Pattern"
showing 10 items of 4203 documents
Fuzzy sigmoid kernel for support vector classifiers
2004
This Letter proposes the use of the fuzzy sigmoid function presented in (IEEE Trans. Neural Networks 14(6) (2003) 1576) as non-positive semi-definite kernel in the support vector machines framework. The fuzzy sigmoid kernel allows lower computational cost, and higher rate of positive eigenvalues of the kernel matrix, which alleviates current limitations of the sigmoid kernel.
How and Why Some Issues Spread Fast In Social Media
2015
This paper brings together current insights from various disciplines into the spreading of social media post. By a structured literature review of peer-reviewed literature 39 recent articles on diffusion in social media were located. The search spanned 10 years, although all the papers were published after 2009, indicating that the examination and observation of spread patterns in social media is still in an early stage. The analys is focused on spread patterns and factors that may explain how rapidly issues spread in the online environment. Based on the findings, from an organizational perspective a model is constructed and directions for future research are suggested. The model focuses on…
Lau Effect And Binary Logic
1989
The Lau effect is applied to implement the whole set of binary logic operations optically. Our technique works with spatially incoherent light and does not require lenses or any other optical accessory.
Automating statistical diagrammatic representations with data characterization
2017
The search for an efficient method to enhance data cognition is especially important when managing data from multidimensional databases. Open data policies have dramatically increased not only the volume of data available to the public, but also the need to automate the translation of data into efficient graphical representations. Graphic automation involves producing an algorithm that necessarily contains inputs derived from the type of data. A set of rules are then applied to combine the input variables and produce a graphical representation. Automated systems, however, fail to provide an efficient graphical representation because they only consider either a one-dimensional characterizat…
Mesh Visual Quality based on the combination of convolutional neural networks
2019
Blind quality assessment is a challenging issue since the evaluation is done without access to the reference nor any information about the distortion. In this work, we propose an objective blind method for the visual quality assessment of 3D meshes. The method estimates the perceived visual quality using only information from the distorted mesh to feed pre-trained deep convolutional neural networks. The input data is prepared by rendering 2D views from the 3D mesh and the corresponding saliency map. The views are split into small patches of fixed size that are filtered using a saliency threshold. Only the salient patches are selected as input data. After that, three pre-trained deep convolu…
Multiple Classifiers and Data Fusion for Robust Diagnosis of Gearbox Mixed Faults
2019
Detection and isolation of single and mixed faults in a gearbox are very important to enhance the system reliability, lifetime, and service availability. This paper proposes a hybrid learning algorithm, consisting of multilayer perceptron (MLP)- and convolutional neural network (CNN)-based classifiers, for diagnosis of gearbox mixed faults. Domain knowledge features are required to train the MLP classifier, while the CNN classifier can learn features itself, allowing to reduce the required knowledge features for the counterpart. Vibration data from an experimental setup with gearbox mixed faults is used to validate the effectiveness of the algorithms and compare them with conventional metho…
Text Classification Using Novel “Anti-Bayesian” Techniques
2015
This paper presents a non-traditional “Anti-Bayesian” solution for the traditional Text Classification (TC) problem. Historically, all the recorded TC schemes work using the fundamental paradigm that once the statistical features are inferred from the syntactic/semantic indicators, the classifiers themselves are the well-established statistical ones. In this paper, we shall demonstrate that by virtue of the skewed distributions of the features, one could advantageously work with information latent in certain “non-central” quantiles (i.e., those distant from the mean) of the distributions. We, indeed, demonstrate that such classifiers exist and are attainable, and show that the design and im…
Interlacing multiplexing techniques for optical morphological correlation
2006
We propose a novel approach to implement nonlinear morphological correlation. Previous implementation was based on a time sequential approach that consists on displaying different binary image decomposition in a joint transform correlator adding each joint power spectra sequentially. A second Fourier transformation of the sum of joint power spectra gives the correlation output. In this paper, we propose to interlace the different binary images into one single distribution. Then, we introduce the distribution in a conventional joint transform correlator. The correlation output gives the morphological correlation at a specific location. The advantage is important considering that no sequentia…
Experimental evaluation of topological-based fitness functions to detect complexes in PPI networks
2012
The detection of groups of proteins sharing common biological features is an important research issue, intensively investigated in the last few years, because of the insights it can give in understanding cell behavior. In this paper we present an extensive experimental evaluation campaign aiming at exploring the capability of Genetic Algorithms (GAs) to find clusters in protein-protein interaction networks, when different topological-based fitness functions are employed. A complete experimentation on the yeast protein-protein interaction network, along with a comparative evaluation of the effectiveness in detecting true complexes on the yeast and human networks, reveals GAs as a feasible an…
2D geon based generic object recognition
2011
The Recognition by Components(RBC) is a theory in Psychology introduced by Biederman in the late 80s, by which humans perceive scenes through simple 3D objects with regular shapes such as spheres, cubes, cylinders, cones, or wedges, called Geons (geometric ions). Extracting geons from 2D images is a very challenging task as it requires a good segmentation and the recognition of the 3D geons in a 2D space. In this paper, we propose a novel approach for extracting 2D geons from 2D images. The process is composed of three major parts: image preprocessing which includes image background removal and segmentation, arc-geon detection, and polygon-geon detection. We also propose a general procedure…