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A Bayesian-based classification framework for financial time series trend prediction
Financial time series have been extensively studied within the past decades; however, the advent of machine learning and deep neural networks opened...
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Source-Free Unsupervised Domain Adaptation
This chapter discusses SFDA, where models trained on labeled source data need to adapt to unlabeled target data without accessing the original source...
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Unsupervised instance selection via conjectural hyperrectangles
Machine learning algorithms spend a lot of time processing data because they are not fast enough to commit huge data sets. Instance selection...
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Progressive spatial–temporal transfer model for unsupervised person re-identification
Over the past decade, a more widespread area of computer vision research has been person re-identification (P-Reid). This technology is applied in...
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Predicting document novelty: an unsupervised learning approach
In the age of information deluge, it is pivotal to have access to information or knowledge which is not just relevant but also, novel. Knowledge...
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Self-corrected unsupervised domain adaptation
Unsupervised domain adaptation (UDA), which aims to use knowledge from a label-rich source domain to help learn unlabeled target domain, has recently...
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Filter feature selection methods for text classification: a review
Filter feature selection methods are utilized to select discriminative terms from high-dimensional text data to improve text classification...
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Unsupervised Learning with Restricted Boltzmann Machines and Autoencoders
Unsupervised learning is a branch of machine learning that tries to find hidden structures within unlabeled data and derive insights from it....
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Improving pseudo-labeling with reliable inter-camera distance encouragement for unsupervised person re-identification
Unsupervised person re-identification (re-ID) aims to train a discriminative model without identity annotations. State-of-the-art methods usually...
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Dynamic bias alignment and discrimination enhancement for unsupervised domain adaptation
Unsupervised domain adaptation (UDA) aims to explore the knowledge of labeled source domain to help training the model of unlabeled target domain. By...
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Automated classification of Alzheimer's disease based on deep belief neural networks
When it comes to the causes of dementia, Alzheimer's disease is the most mysterious. There is no central genetic component connected to Alzheimer's...
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Optimal trained ensemble of classification model for satellite image classification
Satellite image classification is the most significant remote sensing method for computerized analysis and pattern detection of satellite data. This...
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Unsupervised Classification of Categorical Time Series Through Innovative Distances
In this paper, two novel distances for nominal time series are introduced. Both of them are based on features describing the serial dependence...
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An automatic cascaded approach for pancreas segmentation via an unsupervised localization using 3D CT volumes
Automatic organ segmentation using computed tomography (CT) images can support radiologists while carrying out quantitative and qualitative analyses...
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HaarAE: an unsupervised anomaly detection model for IOT devices based on Haar wavelet transform
Given the shortcomings of the existing anomaly detection methods based on IoT devices, including insufficient feature extraction, poor model fitting...
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BCNN: An Effective Multifocus Image fusion Method Based on the Hierarchical Bayesian and Convolutional Neural Networks
AbstractBecause the focus information is obtained under different optical depth, it is impossible to collect all relevant information of objects from...
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Impact of Autotuned Fully Connected Layers on Performance of Self-supervised Models for Image Classification
With the recent advancements of deep learning-based methods in image classification, the requirement of a huge amount of training data is inevitable...
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Efficient hyperparameter tuning for predicting student performance with Bayesian optimization
Higher education is crucial as it introduces students to various fields and then guides them to the next steps. Student’s academic performance is...
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Applications and Techniques of Machine Learning in Cancer Classification: A Systematic Review
The domain of Machine learning has experienced Substantial advancement and development. Recently, showcasing a Broad spectrum of uses like...
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Bayesian optimized novel CNN for improved diagnosis from ultrasound breast tumor images
Convolutional neural networks (CNNs) have played a significant role in feature extraction and tasks thereafter for accurate and automated diagnosis...
