AIMC Topic: Pattern Recognition, Automated

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A Predictive-Coding Network That Is Both Discriminative and Generative.

Neural computation Aug 14, 2020
Predictive coding (PC) networks are a biologically interesting class of neural networks. Their layered hierarchy mimics the reciprocal connectivity pattern observed in the mammalian cortex, and they can be trained using local learning rules that appr...
Forecasting Pattern Recognition, Automated Discrimination Learning Humans Neural Networks, Computer
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Image Target Recognition via Mixed Feature-Based Joint Sparse Representation.

Computational intelligence and neuroscience Aug 10, 2020
An image target recognition approach based on mixed features and adaptive weighted joint sparse representation is proposed in this paper. This method is robust to the illumination variation, deformation, and rotation of the target image. It is a data...
Lighting Pattern Recognition, Automated Wavelet Analysis Neural Networks, Computer Principal Component Analysis
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Twin minimax probability machine for pattern classification.

Neural networks : the official journal of the International Neural Network Society Aug 1, 2020
We propose a new distribution-free Bayes optimal classifier, called the twin minimax probability machine (TWMPM), which combines the benefits of both minimax probability machine(MPM) and twin support vector machine (TWSVM). TWMPM tries to construct t...
Databases, Factual Support Vector Machine Bayes Theorem Pattern Recognition, Automated
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Design and Performance Evaluation of a Deep Neural Network for Spectrum Recognition of Underwater Targets.

Computational intelligence and neuroscience Aug 1, 2020
Due to the complexity of the underwater environment, underwater acoustic target recognition (UATR) has always been challenging. Although deep neural networks (DNN) have been used in UATR and some achievements have been made, the performance is not sa...
Noise Acoustics Datasets as Topic Pattern Recognition, Automated Signal-To-Noise Ratio Deep Learning
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A New Image Classification Approach via Improved MobileNet Models with Local Receptive Field Expansion in Shallow Layers.

Computational intelligence and neuroscience Aug 1, 2020
Because deep neural networks (DNNs) are both memory-intensive and computation-intensive, they are difficult to apply to embedded systems with limited hardware resources. Therefore, DNN models need to be compressed and accelerated. By applying depthwi...
Animals Pattern Recognition, Automated Classification Deep Learning Datasets as Topic
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Improved object recognition using neural networks trained to mimic the brain's statistical properties.

Neural networks : the official journal of the International Neural Network Society Jul 29, 2020
The current state-of-the-art object recognition algorithms, deep convolutional neural networks (DCNNs), are inspired by the architecture of the mammalian visual system, and are capable of human-level performance on many tasks. As they are trained for...
Visual Cortex Neural Networks, Computer Animals Models, Neurological Pattern Recognition, Automated Haplorhini Pattern Recognition, Visual
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Patterns of 1,748 Unique Human Alloimmune Responses Seen by Simple Machine Learning Algorithms.

Frontiers in immunology Jul 28, 2020
Allele specific antibody response against the polymorphic system of HLA is the allogeneic response marker determining the immunological risk for graft acceptance before and after organ transplantation and therefore routinely studied during the patien...
Histocompatibility Testing Isoantigens Graft Survival Isoantibodies Treatment Outcome Histocompatibility Humans Cluster Analysis Kidney Transplantation Machine Learning Flow Cytometry Female Male Immunosuppressive Agents HLA Antigens Adult Middle Aged Principal Component Analysis Graft Rejection Pattern Recognition, Automated
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Unsupervised multi-domain multimodal image-to-image translation with explicit domain-constrained disentanglement.

Neural networks : the official journal of the International Neural Network Society Jul 25, 2020
Image-to-image translation has drawn great attention during the past few years. It aims to translate an image in one domain to a target image in another domain. However, three big challenges remain in image-to-image translation: (1) the lack of large...
Unsupervised Machine Learning Pattern Recognition, Automated Image Processing, Computer-Assisted
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Natural language processing and machine learning to enable automatic extraction and classification of patients' smoking status from electronic medical records.

Upsala journal of medical sciences Jul 22, 2020
BACKGROUND: The electronic medical record (EMR) offers unique possibilities for clinical research, but some important patient attributes are not readily available due to its unstructured properties. We applied text mining using machine learning to en...
Humans Research Design Electronic Health Records Reproducibility of Results Machine Learning Software Algorithms False Positive Reactions Tobacco Use Disorder Data Mining Observer Variation Medical Informatics Automation Pattern Recognition, Automated Smoking Support Vector Machine Natural Language Processing Bayes Theorem Sweden ROC Curve
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Spatial-Frequency Feature Learning and Classification of Motor Imagery EEG Based on Deep Convolution Neural Network.

Computational and mathematical methods in medicine Jul 20, 2020
EEG pattern recognition is an important part of motor imagery- (MI-) based brain computer interface (BCI) system. Traditional EEG pattern recognition algorithm usually includes two steps, namely, feature extraction and feature classification. In feat...
Motor Cortex Mathematical Concepts Imagination Pattern Recognition, Automated Brain-Computer Interfaces Neural Networks, Computer Algorithms Humans Databases, Factual Deep Learning Models, Neurological Electroencephalography
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