This paper reports the study results on neural network training algorithm of numerical optimization techniques multiface detection in static images. The training algorithms involved are scale gradient conjugate backpropagation, conjugate gradient backpropagation with Polak-Riebre updates, conjugate gradient backpropagation with Fletcher-Reeves updates, one secant backpropagation and resilent ba...

The paper proposes a general framework which encompasses the training of neural networks and the adaptation of filters. We show that neural networks can be considered as general non-linear filters which can be trained adaptively, i. e. which can undergo continual training with a possibly infinite number of time-ordered examples. We introduce the canonical form of a neural network. This canonica...

Recently Semi-Supervised learning algorithms such as co-training are used in many application domains. In co-training, two classifiers based on different views of data or on different learning algorithms are trained in parallel and then unlabeled data that are classified differently by the classifiers but for which one classifier has large confidence are labeled and used as training data for th...

New types of machine learning hardware in development and entering the market hold the promise of revolutionizing deep learning in a manner as profound as GPUs. However, existing software frameworks and training algorithms for deep learning have yet to evolve to fully leverage the capability of the new wave of silicon. We already see the limitations of existing algorithms for models that exploi...

abstract- the high production of orange fruit in iran calls for quality sorting of this product as a requirement for entering global markets. this study was devoted to the development of an automatic fruit sorter based on size. the hardware consisted of two units. an image acquisition apparatus equipped with a camera, a robotic arm and controller circuits. the second unit consisted of a robotic...

Online algorithms that process one example at a time are advantageous when dealing with very large data or with data streams. Stochastic Gradient Descent (SGD) is such an algorithm and it is an attractive choice for online Support Vector Machine (SVM) training due to its simplicity and effectiveness. When equipped with kernel functions, similarly to other SVM learning algorithms, SGD is suscept...

ÐWhile numerous page segmentation algorithms have been proposed in the literature, there is lack of comparative evaluationÐempirical or theoreticalÐof these algorithms. In the existing performance evaluation methods, two crucial components are usually missing: 1) automatic training of algorithms with free parameters and 2) statistical and error analysis of experimental results. In this paper, w...

Recently there has been signi cant interest in supervised learning algorithms that combine labeled and unlabeled data for text learning tasks. The co-training setting (Blum & Mitchell, 1998) applies to datasets that have a natural separation of their features into two disjoint sets. We demonstrate that when learning from labeled and unlabeled data, algorithms explicitly leveraging a natural ind...

Artificial neural network (ANN) training is one of the major challenges in using a prediction model based on ANN. Gradient based algorithms are the most frequent training algorithms with several drawbacks. The aim of this paper is to present a method for training ANN. The ability of metaheuristics and greedy gradient based algorithms are combined to obtain a hybrid improved opposition based par...

Most people are familiar with the brio labyrinth game and the challenge of guiding the ball through the maze. The goal of this project was to use this game to create a platform for evaluation of control algorithms. The platform was used to evaluate a few different controlling algorithms, both traditional automatic control algorithms as well as algorithms based on online incremental learning. Th...