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Deep learning techniques have claimed state-of-the-art results in a wide range of tasks, including classification. Despite the promising results, there are limitations for these large networks. In fact, deep neural networks have a poor generalisation performance on small data sets, such as biologic data. This paper describes a new machine learning algorithm for classification tasks. We introduce a...
This paper presents a fast algorithmic method to train convolutional neural network (CNN) classifiers through extreme learning which has been verified on popular datasets on classification and pedestrian detection. CNN has been one of the best classifiers for images and object recognition. However, the Backpropagation (BP) algorithm, mostly used for training CNN, suffers from slow learning, local...
Wide Area Motion Imagery (WAMI) are usually taken from unmaned air vehicles at low frame rates, and having very wide ground coverage. These images serve as rich source for many applications like surveillance, urban planing and traffic monitoring. Thus, understanding WAMI imagery exploitation has been gaining more interest recent years. In this paper, we focus on estimating the pose of vehicles in...
Music structural analysis tasks have an important position in the field of Music information retrieval which require an understanding of how humans process music internally, such as music indexing, music summarization, and similarity analysis. Many schemes have been proposed to analyze the structure of recorded music, however they usually use single feature to detect boundaries of songs and the results...
Convolutional Neural Networks (CNNs) have revolutionized the world of image classification over the last few years, pushing the computer vision close beyond human accuracy. The required computational effort of CNNs today requires power-hungry parallel processors and GP-GPUs. Recent efforts in designing CNN Application-Specific Integrated Circuits (ASICs) and accelerators for System-On-Chip (SoC) integration...
Human pulse recognition is an important part of the objective study of Traditional Chinese Medicine (TCM). In the current human pulse recognition methods, there are many feature extraction algorithms but many are complex and redundancy exists in the features selections. This paper focused on the typical convolutional neural network (CNN), and designed a 9-layer CNN which can be used to human wrist...
Recently, machine learning based single image super resolution (SR) approaches focus on jointly learning representations for high-resolution (HR) and low-resolution (LR) image patch pairs to improve the quality of the super-resolved images. However, due to treat all image pixels equally without considering the salient structures, these approaches usually fail to produce visual pleasant images with...
Convolutional Neural Networks (CNNs) are a variation of feed-forward Neural Networks inspired by the biological process in the visual cortex of animals. The interest in this supervised learning algorithm has rapidly grown in many fields like image and video recognition and natural language processing. Nowadays they have become the state of the art in various applications like mobile robot vision,...
Convolutional Neural Network (CNN) has become a successful algorithm in the region of artificial intelligence and a strong candidate for many applications. However, for embedded platforms, CNN-based solutions are still too complex to be applied if only CPU is utilized for computation. Various dedicated hardware designs on FPGA and ASIC have been carried out to accelerate CNN, while few of them explore...
We designed an electronic nose to classify different Chinese liquors. Kernel entropy component analysis (KECA) was applied to reduce the dimensionality of data sets. In order to avoid the blindness of parameter setting, particle swarm optimization (PSO) algorithm was employed to optimize parameters in KECA. At last, we adopted extreme learning machine (ELM) as a classifier to classify eight kinds...
One kind of Deep Learning models-convolutional neural network, which can reduce the complexity of network structure and the number of parameters to be determined through local receptive fields, weight sharing and pooling operation has achieved state of art results in image classification problems. But this model has gradient diffusion problem, which can cause slow updating of the underlying parameters...
A novel classifier architecture is introduced and its performances are evaluated against state of the art shallow classifiers. Its main advantage consists in a very fast learning ensured by a novelty detection algorithm, selecting a list of prototypes among the training samples, used as centers in a radial basis functions neurons layer. Only the radius of the basis functions is optimized to improve...
Recently, due to the global competition companies active in different industries started to be concerned about the customer churn. With a churn rate of 30%, the telecommunications sector takes the first place on the list. The telecommunications operators need to identify customers who are at risk of churning by implementing predictive models. In this paper, we present an advanced data mining methodology...
Quantitative trading strategies are designed to look for relationships between data about an underlying security and its future price and then to generate alpha on a trading desk. Recent years have witnessed the increasing attention from both academic and corporate sectors on enhancing quantitative trading by machine learning techniques due to their excellent predictive powers, with a few successful...
Conventional iris recognition requires controlled conditions (e.g., close acquisition distance and stop-and-stare scheme) and high user cooperation for image acquisition. Non-cooperative acquisition environments introduce many adverse factors such as blur, off-axis, occlusions and specular reflections, which challenge existing iris segmentation approaches. In this paper, we present two iris segmentation...
Recognition of handwritten numerals has gained much interest in recent years due to its various application potentials. The progress of handwritten Bangla numeral is well behind Roman, Chinese and Arabic scripts although it is a major language in Indian subcontinent and is the first language of Bangladesh. Handwritten numeral classification is a high-dimensional complex task and existing methods use...
In this study, we deemed further to evaluate the performance of Neural Network (NN) and Support Vector Machine (SVM) in classifying the gait patterns between autism and normal children. Firstly, temporal spatial, kinetic and kinematic gait parameters of forty four subjects namely thirty two normal subjects and twelve autism children are acquired. Next, these three category gait parameters acted as...
Convolutional Neural Networks (CNNs) are a particular type of Artificial Neural Networks (ANNs) inspired by cells in the primary visual cortex of animals, and represent the state of the art in image recognition and classification. Nowadays, such supervised learning technique is very popular in Big Data analytics. In this context, due to the huge amount of data to be processed, it is crucial to find...
In nanocrystallography, diffraction images are captured to gain insights into the structure of macromolecules. A new generation of experiments is able to take a vast amount of images in a short time. However, most of the images are not suitable for further research. It is not feasible to store and process all images in a reasonable amount of time. In previous work we proposed algorithms able to distinguish...
Automatic facial image analysis has received considerable research interests due to its important role in computer vision and biometrics. As the key component, face feature is usually extracted under largely controlled environment and learnt for specific tasks which limits its discriminant capability in a multi-task learning scenario. In this paper, we present a novel deeply learnt tree-structured...
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