Boosting has been proven to be effective in improving the generalization of machine learning models in many fields. It is capable of getting high-diversity base learners and getting an accurate ensemble model by combi...
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Boosting has been proven to be effective in improving the generalization of machine learning models in many fields. It is capable of getting high-diversity base learners and getting an accurate ensemble model by combining a sufficient number of weak learners. However, it is rarely used in deep learning due to the high training budget of the neural network. Another method named snapshot ensemble can significantly reduce the training budget, but it is hard to balance the tradeoff between training costs and diversity. Inspired by the ideas of snapshot ensemble and boosting, we propose a method named snapshot boosting. A series of operations are performed to get many base models with high diversity and accuracy, such as the use of the validation set, the boosting-based training framework, and the effective ensemble strategy. Last, we evaluate our method on the computer vision(CV) and the natural language processing(NLP) tasks, and the results show that snapshot boosting can get a more balanced trade-off between training expenses and ensemble accuracy than other well-known ensemble methods.
In this paper,we introduce a novel Multi-scale and Auto-tuned Semi-supervised Deep Subspace Clustering(MAS-DSC)algorithm,aimed at addressing the challenges of deep subspace clustering in high-dimensional real-world da...
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In this paper,we introduce a novel Multi-scale and Auto-tuned Semi-supervised Deep Subspace Clustering(MAS-DSC)algorithm,aimed at addressing the challenges of deep subspace clustering in high-dimensional real-world data,particularly in the field of medical *** deep subspace clustering algorithms,which are mostly unsupervised,are limited in their ability to effectively utilize the inherent prior knowledge in medical *** MAS-DSC algorithm incorporates a semi-supervised learning framework that uses a small amount of labeled data to guide the clustering process,thereby enhancing the discriminative power of the feature ***,the multi-scale feature extraction mechanism is designed to adapt to the complexity of medical imaging data,resulting in more accurate clustering *** address the difficulty of hyperparameter selection in deep subspace clustering,this paper employs a Bayesian optimization algorithm for adaptive tuning of hyperparameters related to subspace clustering,prior knowledge constraints,and model loss *** experiments on standard clustering datasets,including ORL,Coil20,and Coil100,validate the effectiveness of the MAS-DSC *** results show that with its multi-scale network structure and Bayesian hyperparameter optimization,MAS-DSC achieves excellent clustering results on these ***,tests on a brain tumor dataset demonstrate the robustness of the algorithm and its ability to leverage prior knowledge for efficient feature extraction and enhanced clustering performance within a semi-supervised learning framework.
Traffic data imputation is a critical preprocessing step in intelligent transportation systems, enabling advanced transportation services. Despite significant advancements in this field, selecting the most suitable mo...
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A wide variety of complex systems are characterized by interactions of different types involving varying numbers of units. Multiplex hypergraphs serve as a tool to describe such structures, capturing distinct types of...
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We study the optimal memorization capacity of modern Hopfield models and Kernelized Hopfield Models (KHMs), a transformer-compatible class of Dense Associative Memories. We present a tight analysis by establishing a c...
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We propose a Regularized Adaptive Momentum Dual Averaging (RAMDA) algorithm for training structured neural networks. Similar to existing regularized adaptive methods, the subproblem for computing the update direction ...
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Technological advancement has provided huge databases for different genres of image to obtain efficient visual information to satisfy users. The existing algorithms tried to extract the important feature vector from t...
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ISBN:
(数字)9798350352931
ISBN:
(纸本)9798350352948
Technological advancement has provided huge databases for different genres of image to obtain efficient visual information to satisfy users. The existing algorithms tried to extract the important feature vector from the images to analyze the similarities between the query image and dataset image but failed to reach their expected results. The noises generated during model training, overfitting and gradient vanishing problems are the problems faced by the existing models. To overcome the problems of the existing methods, the VGG19 and Gated Recurrent Unit (GRU) with cosine similarity are proposed for content-based image retrieval. The z-score normalization technique was used to standardize the intensity of the images which enhanced the training of the model. The VGG19 algorithm extracted spatial features and the GRU model extracted the temporal features from both database and query images. The cosine similarity metric was used for calculating the similarities of both the images and retrieved based on the feature vector similarities. The proposed VGG19 and GRU with cosine similarity metric has obtained a better average precision of 98.89% for Corel 1 K dataset and 84.90% for Corel 10 K dataset compared to the existing Hybrid Graph-based Gray Level Co-Occurrence Matrix (HGGLCM) model.
Mixture models of Plackett-Luce (PL) - one of the most fundamental ranking models - are an active research area of both theoretical and practical significance. Most previously proposed parameter estimation algorithms ...
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Industrial Control systems (ICS) automate industrial processes but also introduces cybersecurity threats. Intrusion Detection System (IDS) are crucial for detecting cyber-attacks on ICS, yet zero-day attacks are often...
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Early childhood education is a critical stage of education because it may establish the foundation for an individual’s intelligence. Students of this age group are generally unable to remain seated and stud...
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