[PDF] Top 20 Generalizing convolutional neural networks for pattern recognition tasks
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Generalizing convolutional neural networks for pattern recognition tasks
... the two-dimensional (2-D) image topology of input changes. This makes CNN robust against changes of input patterns, including translation, scaling, and rotation. This robustness is due to the built-in invariance feature ... See full document
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Pattern Recognition with Convolutional Neural Networks: Humpback Whale Tails
... deep networks with softmax outputs, since: It puts more emphasis on correct classification and can distinguish better between "almost correctly classified" and "totally wrongly classified" than ... See full document
15
Handwritten Digit Recognition Using Convolutional Neural Networks
... Deep Convolutional Neural Networks (CNNs) becomes one of the most appealing approaches and has been a crucial factor in variety of recent success and challenging machine learning applications such as ... See full document
6
Railway Track Circuit Fault Diagnosis Using Recurrent Neural Networks
... Artificial neural networks have achieved the state-of-the-art performance on several pattern recognition ...Instead, networks are trained to produce their end results directly from the ... See full document
5
Prediction of Heart Diseases on the basis of the Cleveland Database
... disease. Neural Networks are statistical tool inspired from biological neural network which can be used to perform various computational tasks such as pattern recognition, ... See full document
5
Candle Stick Pattern Recognition Using Convolutional Neural Network (ResNet)
... The stock markets are considered one of the biggest opportunities to invest and earn money. Since the return on invest percentage is considered comparatively high than other field of business. Hence a lot of people from ... See full document
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Convolutional Neural Networks for Clothing Image Style Recognition
... image recognition include: Bayesian classification, template matching, ...the recognition of clothing image styles, for example, keywords in style quantification the color attributes and the pattern ... See full document
6
Combination of Multiple Acoustic Models with Multi-scale Features for Myanmar Speech Recognition
... speech recognition (ASR) technique is widely used for transcribing audio speech to ...deep neural network (DNN-HMM) framework, DNNs have been proposed to replace GMMs to compute state observation ... See full document
10
Assessing the Corpus Size vs Similarity Trade off for Word Embeddings in Clinical NLP
... classification tasks such as part-of-speech tagging and chunking (Huang et ...entity recognition (Chiu and Nichols, 2016; Lample et ...recurrent neural net- works ...on convolutional ... See full document
10
Pattern Recognition with Convolutional Neural Networks: Humpback Whale Tails
... a neural network does not reside exclusively in the architecture that it may have, but that the data set available substantially alters the final result, being a balanced dataset and a large number of samples a ... See full document
16
Deep Kernel based Convolutional Neural Networks for Image Recognition
... Machine learning at its most basic is the practice of using algorithm to parse data, learn from it and then make a determination or prediction .So rather than hand coding software routines with a specific set of ... See full document
7
Phone recognition with hierarchical convolutional deep maxout networks
... Deep convolutional neural networks (CNNs) have recently been shown to outperform fully connected deep neural networks (DNNs) both on low-resource and on large-scale speech ...that ... See full document
13
Asymmetric 3D Convolutional Neural Networks for Action Recognition
... full length content of the videos to produce large performance improvements over previously results. The Two-stream LSTM [46] stacks multiple LSTM lay- ers to capture dynamic information in a hierarchical manner. The two ... See full document
36
Handwritten Digit Recognition using Convolutional Neural Networks
... called Convolutional network is taken into consideration. A Convolutional network has a benefit over other Artificial Neural networks in extracting and utilizing the features data, enhancing ... See full document
6
Vision based human action recognition using machine learning techniques
... tion recognition based on the unsupervised deep learning method presented in chapter 5, and briefly discussed ...high recognition results in different computer vision ... See full document
173
The Application of Neural Network in Multiple Object Tracking
... Deep neural networks have been successfully applied to image classification [5], object detection [6], image annotation [7] and other fields, and achieved reliable ...deep neural network learns more ... See full document
7
Hand Gesture Recognition System using Convolutional Neural Networks
... This work study and examined how to outwardly perceive every single static motion of American Sign Language (ASL) with uncovered hand. Diverse clients have diverse hand shapes and skin hues, making it progressively ... See full document
7
Learning temporal information from spatial information using capsnets for human action recognition
... Convolutional Neural Networks (CNNs) have been shown to attain outstanding results for human action recognition (HAR) [1] by employing either 2D/3D convolutional kernels [2], [3], or ... See full document
6
Inter-Class Angular Loss for Convolutional Neural Networks
... Baseline Networks To show the adaptability of ICAL to different networks with different classification tasks, we apply ICAL to two types of convolutional neural networks (CNNs) ... See full document
8
CV dwlee(new)
... Kim, B.-H., Lee, D., and Lee, J. (2006.05) Local Volatility Function Approximation Using Recon- structed Radial Basis Function Networks. Lecture Notes in Computer Science 3973:524-530. Cho, C., Kim, S., Lee, J., ... See full document
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