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AFMPM: adaptive feature map pruning method based on feature distillation
Feature distillation is a technology that uses the middle layer feature map of the teacher network as knowledge to transfer to the students. The...
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Analysis and Evaluation of Feature Selection and Feature Extraction Methods
Hand gestures are widely used in human-to-human and human-to-machine communication. Therefore, hand gesture recognition is a topic of great interest....
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Post-processing feature-map** topology optimisation designs towards feature-based CAD processing
Feature-map** (FM) optimisation frameworks have received much attention for structural topology optimisation with explicit geometric parameters....
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Feature selection for label distribution learning under feature weight view
Label Distribution Learning (LDL) is a fine-grained learning paradigm that addresses label ambiguity, yet it confronts the curse of dimensionality....
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Person Re-Identification Based on Spatial Feature Learning and Multi-Granularity Feature Fusion
In view of the weak ability of the convolutional neural networks to explicitly learn spatial invariance and the probabilistic loss of discriminative...
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Resformer: Local Frame-Level Feature and Global Segment-Level Feature Joint Learning for Speaker Verification
In this paper, we propose a hybrid network structure to achieve more discriminant feature representations for speaker recognition, termed Resformer,...
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Premature Ventricular Contractions Detection by Multi-Domain Feature Extraction and Auto-Encoder-based Feature Reduction
Cardiovascular disorders are known to be among the most severe diseases and the leading causes of mortality all over the globe. Premature ventricular...
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Manufacturing feature recognition method based on graph and minimum non-intersection feature volume suppression
Automatic manufacturing feature recognition (AFR) is a critical technology for realizing CAD/CAPP/CAM integration in the era of intelligent...
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Singer identification model using data augmentation and enhanced feature conversion with hybrid feature vector and machine learning
Analyzing songs is a problem that is being investigated to aid various operations on music access platforms. At the beginning of these problems is...
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Feature Ranking for Feature Sorting and Feature Selection, and Feature Sorting: FR4(FSoFS) \(\wedge \) FSo
This paper introduces the application of feature ranking with a twofold purpose: first it achieves a feature sorting which becomes into a feature...
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Deep Feature selection
Feature selection plays a crucial role in machine learning by identifying the most relevant and informative features from the input data, leading to...
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Hybrid genetic optimization for quantum feature map design
Kernel methods are an import class of techniques in machine learning. To be effective, good feature maps are crucial for map** non-linearly...
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Discriminative multi-scale adjacent feature for person re-identification
Recently, discriminative and robust identification information has played an increasingly critical role in Person Re-identification (Re-ID). It is a...
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Automatic feature recognition from STEP file for smart manufacturing
Industrial organizations are increasingly adopting computer-aided manufacturing (CAM) and computer-aided design (CAD) tools to streamline...
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Exploiting multi-scale hierarchical feature representation for visual tracking
Convolutional neural networks (CNNs) have been the dominant architectures for feature extraction tasks, but CNNs do not look for and focus on some...
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A novel feature selection approach with integrated feature sensitivity and feature correlation for improved prediction of heart disease
This paper presents a random forest-feature sensitivity and feature correlation (RF-FSFC) technique for enhanced heart disease prediction. The...
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TFITrack: Transformer Feature Integration Network for Object Tracking
Due to the ignoring of rich spatio-temporal and global contextual information with convolutional neural networks in features extraction, the...
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A novel learning method for feature evolvable streams
Recently, many researchers have focused on a novel type of data stream known as a feature evolvable stream, wherein the existing features may become...
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Algorithms to estimate Shapley value feature attributions
Feature attributions based on the Shapley value are popular for explaining machine learning models. However, their estimation is complex from both...
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Intelligent Feature Engineering and Feature Selection Techniques for Machine Learning Evaluation
Manual feature engineering can take a long time and be ineffective at capturing complicated patterns, while choosing the wrong features can produce...