Training Time Complexity Research Articles

Recognition of early phase of atherosclerosis using principles component analysis and artificial neural networks from carotid artery Doppler signals

Abstract Atherosclerosis means thickening and hardening of the arteries, which has dramatic effects on blood pressure, resistance and blood flow. Since angiography is invasive and has a relatively high cost, non-invasive ultrasonic Doppler sonography is generally recommended to diagnose of athersosclerosis. In this study, we have employed the sonograms depicted from Autoregressive (AR) modeling, Principles component analysis (PCA) for data reduction of Doppler sonograms and artificial neural networks (ANN) in order to distinguish between atherosclerosis and healthy subjects. The fuzzy appearance of the carotid artery Doppler signals makes physicians suspicious about the existence of diseases and causes false diagnosis. Our technique gets around this problem using ANN to decide and assist the physician to make the final judgment in confidence. The stated results show that training time and processing complexity were reduced using PCA-ANN architecture however the proposed method can make an effective interpretation and ANN classified Doppler signals successfully.

A systematic and effective supervised learning mechanism based on Jacobian rank deficiency.

Most neural network applications rely on the fundamental approximation property of feedforward networks. Supervised learning is a means of implementing this approximate mapping. In a realistic problem setting, a mechanism is needed to devise this learning process based on available data, which encompasses choosing an appropriate set of parameters in order to avoid overfitting, using an efficient learning algorithm measured by computation and memory complexities, ensuring the accuracy of the training procedures as measured by the training error, and testing and cross-validation for generalization. We develop a comprehensive supervised learning algorithm to address these issues. The algorithm combines training and pruning into one procedure by utilizing a common observation of Jacobian rank deficiency in feedforward networks. The algorithm not only reduces the training time and overall complexity but also achieves training accuracy and generalization capabilities comparable to more standard approaches. Extensive simulation results are provided to demonstrate the effectiveness of the algorithm.

A neural synaptic compression codec for efficient image transmission

For efficient cellular communication channel usage, we propose a neural computation model for image coding. In a constant-time unsupervised learning, our neural model approximates optimal pattern clustering from training example images through a memory adaptation process, and builds a compression codebook in its synaptic weight matrix. This neural codebook can be distributed to both ends of a transmission channel for fast codec operations on general images. The transmission is merely the indices of the codebook entries best matching the patterns in the image to be transmitted. These indices can further be compressed through a classical entropy coding method to yield even more transmission reduction. Other advantages of our model are the low training time complexity, high utilization of neurons, robust pattern clustering capability, and simple computation. A VLSI implementation is also highly suitable for the intrinsic parallel nature of neural networks. Our compression results are competitive compared to JPEG and wavelet methods. We also reveal the general codebook's cross-compression results, filtering effects by special training methods, and learning enhancement techniques for obtaining a compact codebook to yield both high compression and picture quality.

Issues on representational capabilities of artificial neural networks and their implementation

This work is an organized review on the representational capabilities of artificial neural networks and the questions that arise in their implementation. It covers the Kolmogorov's superposition theorem and different statements regarding how it could be related to the representational power of neural networks. Generalization capability of neural networks is then considered and methods of improving this capability are discussed. Some theorems and statements concerning the bound on the number of hidden layers, form of the activation function, and time complexity of training of neural networks are other subjects of this article. © 1995 John Wiley & Sons, Inc.

Development of a personal-computer-based intelligent tutoring system

Gaussian kernel Support Vector Machines (SVMs) deliver state-of-the-art generalization performance for non-linear classification, but the time complexity of their training process is at least quadratic w.r.t. the size of training set, preventing them from scaling up on large datasets. To address this issue, we propose a novel approach to large-scale kernel SVMs in sublinear time w.r.t. the size of training set, which combines three well-known and efficient techniques with theoretical guarantees. First, we subsample massive samples to reduce the sample size. Then, we use the random Fourier feature mapping on the subsamples to construct explicit random feature space where we can train a linear SVM to approximate the corresponding Gaussian kernel SVM. Finally, we use parallel algorithms to make our approach more scalable. Deriving the upper bounds of kernel matrix approximation error, hypothesis error and excess risk w.r.t. the size of training set and the dimension of random feature space, we establish the theoretical foundation of our proposed approach. In this way, we can reduce the time complexity of training kernel SVMs without sacrificing much accuracy. Our proposed approach achieves high accuracy and has sublinear training time complexity, which exhibits good scalability theoretically and empirically.