Comparing Performance of Neural Networks Applied to a Simplified Recognition Problem
نویسندگان
چکیده
In this note we present and discuss results of experiments comparing the performance of six neural network architectures (back propagation, recurrent network with dampened feedback, network with multiple hidden layers each with a different activation function, jump connection networks, probabilistic neural networks and general regression neural networks) applied to a simplified multi-font recognition problem.
منابع مشابه
Comparing Performance of Neural Networks Recognizing Machine Generated Characters
Neural networks are a popular tool in the area of pattern recognition. However, since a very large number of neural network architectures exist, it has not been established which one is the most efficient. In this paper we compare the performance of three neural network architectures: Kohonen’s self-organizing network, probabilistic neural network, and a modified backpropagation applied to a si...
متن کاملIntroducing a method for extracting features from facial images based on applying transformations to features obtained from convolutional neural networks
In pattern recognition, features are denoting some measurable characteristics of an observed phenomenon and feature extraction is the procedure of measuring these characteristics. A set of features can be expressed by a feature vector which is used as the input data of a system. An efficient feature extraction method can improve the performance of a machine learning system such as face recognit...
متن کاملشبکه عصبی پیچشی با پنجرههای قابل تطبیق برای بازشناسی گفتار
Although, speech recognition systems are widely used and their accuracies are continuously increased, there is a considerable performance gap between their accuracies and human recognition ability. This is partially due to high speaker variations in speech signal. Deep neural networks are among the best tools for acoustic modeling. Recently, using hybrid deep neural network and hidden Markov mo...
متن کاملPersian Phone Recognition Using Acoustic Landmarks and Neural Network-based variability compensation methods
Speech recognition is a subfield of artificial intelligence that develops technologies to convert speech utterance into transcription. So far, various methods such as hidden Markov models and artificial neural networks have been used to develop speech recognition systems. In most of these systems, the speech signal frames are processed uniformly, while the information is not evenly distributed ...
متن کاملSteel Consumption Forecasting Using Nonlinear Pattern Recognition Model Based on Self-Organizing Maps
Steel consumption is a critical factor affecting pricing decisions and a key element to achieve sustainable industrial development. Forecasting future trends of steel consumption based on analysis of nonlinear patterns using artificial intelligence (AI) techniques is the main purpose of this paper. Because there are several features affecting target variable which make the analysis of relations...
متن کامل