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Optimized Bayesian adaptive resonance theory map** model using a rational quadratic kernel and Bayesian quadratic regularization
Bayesian adaptive resonance theory (ART) and ARTMAP-based neural network classifier (known as BAM) are widely used and achieve good classification...
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STUDD : a student–teacher method for unsupervised concept drift detectionConcept drift detection is a crucial task in data stream evolving environments. Most of state of the art approaches designed to tackle this problem...
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Supervised Classification
Chapter 5 covers supervised k-nearest neighbors’ classification, naïve Bayes probabilistic learning, and divide and conquer decision tree...
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Inferencing transportation mode using unsupervised deep learning approach exploiting GPS point-level characteristics
Discovering the mode of transportation is a fundamental and challenging step in the various transportation analysis problems such as travel demand...
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Unsupervised Fraud Detection on Sparse Rating Networks
Network fraud detection, specifically identifying abnormal users on rating platforms, has attracted considerable interests of researchers due to its...
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A Unified Approach to Learning with Label Noise and Unsupervised Confidence Approximation
Noisy label training is the problem of training a neural network from a dataset with errors in the labels. Selective prediction is the problem of...
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Heterogeneous clustering via adversarial deep Bayesian generative model
This paper aims to study the deep clustering problem with heterogeneous features and unknown cluster number. To address this issue, a novel deep...
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Unsupervised discretization by two-dimensional MDL-based histogram
Unsupervised discretization is a crucial step in many knowledge discovery tasks. The state-of-the-art method for one-dimensional data infers locally...
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Bayesian contiguity constrained clustering
Clustering is a well-known and studied problem, one of its variants, called contiguity-constrained clustering, accepts as a second input a graph used...
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Post-hoc Rule Based Explanations for Black Box Bayesian Optimization
Explainable Artificial Intelligence (XAI) aims to enhance transparency and trust in AI systems by providing insights into their decision-making...
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Domain-Agnostic Priors for Semantic Segmentation Under Unsupervised Domain Adaptation and Domain Generalization
In computer vision, an important challenge to deep neural networks comes from adjusting the varying properties of different image domains. To study...
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Unsupervised anomaly detection based method of risk evaluation for road traffic accident
Elevated road plays a very important role as corridors in urban traffic network, and the occurrence of traffic accidents often causes a great impact....
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Interpretable Bayesian network abstraction for dimension reduction
Dimension reduction methods is effective for tackling the complexity of models learning from high-dimensional data. Usually, they are presented as a...
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A new hybrid model of convolutional neural networks and hidden Markov chains for image classification
Convolutional neural networks (CNNs) have lately proven to be extremely effective in image recognition. Besides CNN, hidden Markov chains (HMCs) are...
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A Bayesian sampling framework for asymmetric generalized Gaussian mixture models learning
This paper proposes an effective unsupervised Bayesian framework for learning a finite mixture of asymmetric generalized Gaussian distributions...
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Transfer Learning Fusion and Stacked Auto-encoders for Viral Lung Disease Classification
The objective of this research endeavor is to identify an effective model for the classification of multiple viral respiratory diseases, encompassing...
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An Efficient Hybrid Classifier for MRI Brain Images Classification Using Machine Learning Based Naive Bayes Algorithm
In recent days, advanced techniques are used to compare the analysis of medical images, identifying, pre-processing and interpreting the images. As a...
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Detecting Air Conditioning Usage in Households Using Unsupervised Machine Learning on Smart Meter Data
This article presents an unsupervised machine learning approach for the problem of detecting use of air conditioning in households, during the...
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bi-directional Bayesian probabilistic model based hybrid grained semantic matchmaking for Web service discovery
Web service discovery is a fundamental task in service-oriented architectures which searches for suitable web services based on users’ goals and...
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Feature Embedding Representation for Unsupervised Speaker Diarization in Telephone Calls
Speaker diarization aims to segment an audio recording, where different speakers are involved, into speech segments based on the speaker's identity....