A method for feature extraction which makes use of feedforward neural networks with a single hidden layer is presented. The topology of the networks is determined by a network construction algorithm and a network pruning algorithm. Network construction is achieved by having just 1 hidden unit ...
NetworksPursuit coursesFeature ExtractionThe paper suggests a statistical framework for the parameter estimation problem associated with unsupervised learning in a neural network, leading to an exploratory projection pursuit network that performs feature extraction, or dimensionality reduction. Neural networks; ...
The main idea behind the proposed approach is to use deep networks as feature learners only, but not classifiers, and then utilize feature selection to determine a small set of neurons that provide maximal information for an efficient activity recognizer. This approach combines elements from standard...
A comparative study of neural network based feature extraction paradigms Pattern Recognition Lett. (1999) S. Sakaue Reduction of required precision bits for backpropagation applied to pattern recognition IEEE Trans. Neural Networks (1993) M. Hoehfeld et al. Learning with limited numerical precision ...
Static, linear, nonlinear, dynamic and recurrent networks are analyzed for time series prediction of resource's performances. Recurrent networks combined with wavelet feature extraction process resulted best predictions 展开 关键词: distributed processing feature extraction recurrent neural nets resource ...
论文题目《Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks》 论文作者:Y ushi Chen, Member , IEEE, Hanlu Jiang, Chunyang Li, Xiuping Jia, Senior Member , IEEE, and Pedram Ghamisi, Member , IEEE ...
In this paper we propose an unsupervised learning algorithm for neural networks that are used in feature extraction problem. These learning algorithms use genetic algorithm as a searching technique for global minimum of error performance surface and LMS algorithm for final convergence to the global ...
Feature extraction. Neural networks can automatically learn and extract relevant features from raw data, which simplifies the modeling process. However, traditional ML methods differ from neural networks in the sense that they often require manual feature engineering. Information storage. ANNs store inform...
论文名:《MIT at SemEval-2017 Task 10:Relation Extraction with Convolutional Neural Networks》 src:arxiv.org dataset:ScienceIE 摘要:近年来科学领域的学术文章构成了一个很大的知识库,其中每个实体可以通过科学概念之间的关系来推断,诸如同义词和下义词。本文要抽取的关系分为三种:无关系、同义词、下义词,图...
Code Reference GitHub - aa-samad/conv_snn: Code for "Convolutional spiking neural networks (SNN) for spatio-temporal feature extraction" papergithub.com/aa-samad/conv_snn btw,这篇文章的代码里面对于dvs数据集的处理部分可以借鉴一下,比较有用~...