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Kernel fusion is a popular and effective approach for combining multiple features that characterize different aspects of data. Traditional approaches for Multiple Kernel Learning (MKL) attempt to learn the parameters for combining the kernels through sophisticated optimization procedures. In this paper, we propose an alternative approach that creates dense embeddings for data using the kernel similarities...
This article proposes a performance analysis of kernel least squares support vector machines (LS-SVMs) based on a random matrix approach, in the regime where both the dimension of data p and their number n grow large at the same rate. Under a two-class Gaussian mixture model for the input data, we prove that the LS-SVM decision function is asymptotically normal with means and covariances shown to...
This paper presents a method for the classification of Landsat Multi-Spectral Scanner (MSS) satellite images to identify the areas of land use. The image is pre-processed and classified using Support Vector Machine (SVM) with the Radial Basis Function (RBF) Kernel as it is an efficient supervised-classification technique. In this research, pixel — base classification method is performed according...
Convolution neural networks (CNNs) are the heart of deep learning applications. Recent works PRIME [1] and ISAAC [2] demonstrated the promise of using resistive random access memory (ReRAM) to perform neural computations in memory. We found that training cannot be efficiently supported with the current schemes. First, they do not consider weight update and complex data dependency in training procedure...
The breast cancer is one of the most popular cause of death among women. It is also one of the diseases that can be cured and has high healing chances when it is detected in the early stages [1]. Detecting the cancer and differentiating between the diagnosis that affirm whether a patient has breast cancer or not has been considered as a big challenge. In order to have an accurate diagnosis, Support...
Predicting the survival status of patients who will undergo breast cancer surgery is highly important, where it indicates whether conducting a surgery is the best solution for the presented medical case or not. Since this is a case of life or death, the need to explore better prediction techniques to ensure accurate survival status prediction cannot be overemphasized. In this paper we evaluate the...
Given the harsh working conditions such as high-speed flow rate, turbid watch, and steep terrain, it is a very challenging task to find submerged bodies in disaster site occurred at sea or river or for the military purpose. Therefore, if it is possible to utilize the unmanned robot, such as the USV(Unmanned Surface Vehicle) and UUV (Unmanned Underwater Vehicle) for the navigational operation of these...
In this work we study the problem of efficient non-parametric estimation for non-linear time-space dynamic Gaussian processes (GP). We propose a systematic and explicit procedure to address this problem by pairing GP regression with Kalman Filtering. Under a specific separability assumption of the modeling kernel and periodic sampling on a (possibly non-uniform) space-grid, we show how to build an...
Over the past decades, prediction of costumers' purchase behavior has been significantly considered, and completely recognized as one of the most significant research topics in consumer behavior researches. While we attempt to measure response of purchase intention to the contextual factors such as customers' age, gender and income, product price and sale promotion, most of business models are basing...
Recently, deep learning became very popular, and was applied to many fields. The convolutional neural networks are often used for representing the layers for deep learning. In this paper, we propose Convolutional Self Organizing Map, which can be applicable to deep learning. Conventional Self Organizing Map uses single layered architecture, and can visualizes and classifies the input data on 2 dimensional...
Dengue virus infection or dengue fever is caused by the dengue virus (DENV). It is transmitted to humans by mosquitoes. There are four serotypes classified together based on their surface antigens. Each serotype can provide specific immunity and short-term cross-immunity in human. Several studies have examined the classification of dengue molecules into four major classes including methods such as...
In this paper, we explore the usage of deep learning based solutions in fine grained activity recognition in the wild. As a powerful tool, deep learning has been widely used in image classification, object detection and activity recognition. We focus on implementing deep learning methods into the more complicated fine grained activity recognition problems. We test our solutions on MPII activity dataset...
Extreme learning machine (ELM) and support vector machine (SVM) classifiers are developed to detect rales (a gurgling sound that is a symptom of respiratory diseases in poultry). These classifiers operate on Mel-scaled spectral features calculated from recordings of healthy and sick chickens during a vaccine trial. Twenty minutes of labeled data were used to train and test the classifiers, then they...
Deep Learning (DL), especially Convolutional Neural Networks (CNN), has become the state-of-the-art for a variety of pattern recognition issues. Technological developments have allowed the use of high-end General Purpose Graphic Processor Units (GPGPU) for accelerating numerical problem solving. They resort no only to lower computational time, but also allow considering much larger networks. Hence,...
Risk permeates all aspects of doing business. However, support tools capable of systematically identifying the complete spectrum of risks that a company might face are currently lacking. Such a tool would need to reliably identify company-risk relationships from unstructured sources, therefore providing a qualitative assessment of risk exposure. We propose a supervised learning approach that combines...
As the high-tech production system gets more complex, Equipment Condition Diagnosis (ECD) in semiconductor manufacturing for Fault Detection and Classification (FDC) is becoming more and more challenging than ever. This paper uses well-known machine learning techniques such as Support Vector Machine (SVM), K-Means clustering and Self-Organizing Map (SOM) to develop an efficient ECD model. The process...
Monitoring high performance computing systems has become increasingly difficult as researchers and system analysts face the challenge of synthesizing a wide range of monitoring information in order to detect system problems on ever larger machines. We present a method for anomaly detection on syslog data, one of the most important data streams for determining system health. Syslog messages pose a...
Enhancers are crucial to the understanding of mechanisms underlying gene transcriptional regulation. Although having been successfully applied in such projects as ENCODE and Roadmap to generate landscape of enhancers in human cell lines, high-throughput biological experimental techniques are still costly and time consuming for even larger scale identification of enhancers across a variety of tissues...
Deep learning frameworks have recently gained widespread popularity due to their highly accurate prediction capabilities and availability of low cost processors that can perform training over a large dataset quickly. Given the high core count in modern generation high performance computing systems, training deep networks over large data has now become practical. In this work, while targeting the Computational...
Deep learning is a rather new approach to machine learning that has achieved remarkable results in a large number of different image processing applications. Lately, application of deep learning to detect and classify spectral and spatio-spectral signatures in hyperspectral images has emerged. The high dimensionality of hyperspectral images and the limited amount of labelled training data makes deep...
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