Suchergebnisse - Computer Learning AND Pattern Recognition::Neural Networks

Parallel ensemble learning of convolutional neural networks and local binary patterns for face recognition von Tang, Jialin, Su, Qinglang, Su, Binghua, Fong, Simon, Cao, Wei, Gong, Xueyuan

“… A new face recognition method based on parallel ensemble learning of convolutional neural networks (CNN …”
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Deep learning-based BCI for Automatic Imagined Speech Recognition using SPWVD von Kamble, Ashwin, Ghare, Pradnya, Kumar, Vinay

“… In recent years, deep learning with convolutional neural networks (CNN) has transformed computer vision and can perform pattern recognition better than traditional ML-based algorithms …”
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Automatic Recognition of Speed Limits on Speed-Limit Signs by Using Machine Learning von Miyata, Shigeharu

“… ) a method for recognition of the extracted numbers using a neural network. The method of traffic sign recognition previously proposed by the author consisted …”
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A survey of robust adversarial training in pattern recognition: Fundamental, theory, and methodologies von Qian, Zhuang, Huang, Kaizhu, Wang, Qiu-Feng, Zhang, Xu-Yao

“… Deep neural networks have achieved remarkable success in machine learning, computer vision, and pattern recognition in the last few decades …”
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Comparison of Different Learning Algorithms for Pattern Recognition with Hopfield's Neural Network von Szandała, Tomasz

“… Hopfield neural networks can be used for compression, approximation, steering. But they are most commonly used for pattern recognition thanks to their associative memory trait …”
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Grape detection with convolutional neural networks von Cecotti, Hubert, Rivera, Agustin, Farhadloo, Majid, Pedroza, Miguel A.

“… •Evaluation in 3 feature spaces: color and texture, texture only, and color only. Convolutional neural networks, as a type of deep learning approach, have revolutionized …”
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Synchronization of Fractional Stochastic Neural Networks: An Event Triggered Control Approach von Subramaniam, Sasikala, Lim, Chee Peng, Rajan, Rakkiyappan, Mani, Prakash

“… NN play a significant role in the machine learning and deep learning domains that include pattern recognition, computer-vision and so on …”
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A study on giant panda recognition based on images of a large proportion of captive pandas von Chen, Peng, Swarup, Pranjal, Matkowski, Wojciech Michal, Kong, Adams Wai Kin, Han, Su, Zhang, Zhihe, Rong, Hou

“… As a highly endangered species, the giant panda (panda) has attracted significant attention in the past decades. Considerable efforts have been put on panda …”
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Machine learning techniques for state recognition and auto-tuning in quantum dots von Kalantre, Sandesh S., Zwolak, Justyna P., Ragole, Stephen, Wu, Xingyao, Zimmerman, Neil M., Stewart, M. D., Taylor, Jacob M.

“… Fortunately, machine learning techniques for pattern recognition and image classification, using so-called deep neural networks, have shown surprising successes for computer-aided understanding of complex systems …”
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A novel hand gesture detection and recognition system based on ensemble-based convolutional neural network von Sen, Abir, Mishra, Tapas Kumar, Dash, Ratnakar

“… ) environment, and robotics. But detection of the hand portion has become a challenging task in computer vision and pattern recognition communities …”
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Adaptive Feature Selection and Image Classification Using Manifold Learning Techniques von Ashraf, Amna, Nawi, Nazri Mohd, Aamir, Muhammad

“… and is widely used in computer vision, image processing, pattern recognition, neural networks, and machine learning …”
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ADAPTIVE VISION AI von Vodyanitskyi, V., Yuskovych-Zhukovska, V.

“… Computer vision technologies rely on pattern recognition, machine learning, and neural networks to allow computers to break down images, interpret data, and identify features …”
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A survey of deep neural network architectures and their applications von Liu, Weibo, Wang, Zidong, Liu, Xiaohui, Zeng, Nianyin, Liu, Yurong, Alsaadi, Fuad E.

“… with successful applications to computer vision, pattern recognition, speech recognition, natural language …”
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TensorFlow-Based Automatic Personality Recognition Used in Asynchronous Video Interviews von Suen, Hung-Yue, Hung, Kuo-En, Lin, Chien-Liang

“… , and psychological assessment. Advances in computer vision and pattern recognition based on deep learning (DL …”
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1D convolutional neural networks for chart pattern classification in financial time series von Liu, Liying, Si, Yain-Whar

“… Convolutional neural networks (CNNs) have made notable achievements in image recognition and computer vision applications …”
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Spiking neural networks for handwritten digit recognition—Supervised learning and network optimization von Kulkarni, Shruti R., Rajendran, Bipin

“… We demonstrate supervised learning in Spiking Neural Networks (SNNs) for the problem of handwritten digit recognition using the spike triggered Normalized Approximate Descent (NormAD) algorithm …”
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ResPrune: An energy-efficient restorative filter pruning method using stochastic optimization for accelerating CNN von Jayasimhan, Anusha, P., Pabitha

“… Convolutional Neural Networks (CNNs) are frequently employed for image pattern recognition and other computer vision tasks …”
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Progressive Tandem Learning for Pattern Recognition With Deep Spiking Neural Networks von Wu, Jibin, Xu, Chenglin, Han, Xiao, Zhou, Daquan, Zhang, Malu, Li, Haizhou, Tan, Kay Chen

“… However, the training of deep SNNs is not straightforward. In this paper, we propose a novel ANN-to-SNN conversion and layer-wise learning framework for rapid and efficient pattern recognition, which is referred to as progressive tandem learning …”
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Recreating Fingerprint Images by Convolutional Neural Network Autoencoder Architecture von Saponara, Sergio, Elhanashi, Abdussalam, Zheng, Qinghe

“… Deep learning, especially Convolutional Neural Network (CNN) has made a tremendous success in the field of computer vision for pattern recognition …”
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CMT: Convolutional Neural Networks Meet Vision Transformers von Guo, Jianyuan, Han, Kai, Wu, Han, Tang, Yehui, Chen, Xinghao, Wang, Yunhe, Xu, Chang

“… However, there are still gaps in both performance and computational cost between transformers and existing convolutional neural networks (CNNs …”
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