Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2016; 25th International C Alessandro E.P. Villa,Paolo Masulli,Antonio Javier Confe

整理

audiologist

Throttle

intention

Keyword Spotting with Convolutional Deep Belief Networks and Dynamic Time Warpingworks and using Dynamic Time Warping for word scoring. Features are learned from word images, in an unsupervised manner, using a sliding window to extract horizontal patches. For two single writer historical data sets, it is shown that the proposed learned feature extractor outperforms two standard

同來核對(duì)

Computational Advantages of Deep Prototype-Based Learningdel but at a fraction of the computational cost, especially w.r.t. memory requirements. As prototype-based classification and regression methods are typically plagued by the exploding number of prototypes necessary to solve complex problems, this is an important step towards efficient prototype-base

沙發(fā)

Deep Convolutional Neural Networks for Classifying Body Constitutionoblem of standardizing constitutional classification has become a constraint on the development of Chinese medical constitution. Traditional recognition methods, such as questionnaire and medical examination have the shortcoming of inefficiency and low accuracy. We present an advanced deep convoluti

Comprise

Feature Extractor Based Deep Method to Enhance Online Arabic Handwritten Recognition Systemit handcrafted features based on beta-elliptic model and automatic features using deep classifier called Convolutional Deep Belief Network (CDBN). The experiments are conducted on two different Arabic databases: LMCA and ADAB databases which including respectively isolated characters and Tunisian na

AV-node

Hemodialysis

absorbed

Tactile Convolutional Networks for Online Slip and Rotation Detectionetwork layouts and reached a final classification rate of more than 97?%. Using consumer class GPUs, slippage and rotation events can be detected within 10?ms, which is still feasible for adaptive grasp control.