Co-saliency detection via a self-paced multiple-instance learning framework
As an interesting and emerging topic, co-saliency detection aims at simultaneously
extracting common salient objects from a group of images. On one hand, traditional co …
extracting common salient objects from a group of images. On one hand, traditional co …
Leveraging prior-knowledge for weakly supervised object detection under a collaborative self-paced curriculum learning framework
Weakly supervised object detection is an interesting yet challenging research topic in
computer vision community, which aims at learning object models to localize and detect the …
computer vision community, which aims at learning object models to localize and detect the …
Density-weighted support vector machines for binary class imbalance learning
BB Hazarika, D Gupta - Neural Computing and Applications, 2021 - Springer
In real-world binary classification problems, the entirety of samples belonging to each class
varies. These types of problems where the majority class is notably bigger than the minority …
varies. These types of problems where the majority class is notably bigger than the minority …
Robust statistics-based support vector machine and its variants: a survey
M Singla, KK Shukla - Neural Computing and Applications, 2020 - Springer
Support vector machines (SVMs) are versatile learning models which are used for both
classification and regression. Several authors have reported successful applications of SVM …
classification and regression. Several authors have reported successful applications of SVM …
SMOTE based class-specific extreme learning machine for imbalanced learning
BS Raghuwanshi, S Shukla - Knowledge-Based Systems, 2020 - Elsevier
Imbalanced learning is one of the substantial challenging problems in the field of data
mining. The datasets that have skewed class distribution pose hindrance to conventional …
mining. The datasets that have skewed class distribution pose hindrance to conventional …
KNN weighted reduced universum twin SVM for class imbalance learning
In real world problems, imbalance of data samples poses major challenge for the
classification problems as the data samples of a particular class are dominating. Problems …
classification problems as the data samples of a particular class are dominating. Problems …
A survey of accelerating parallel sparse linear algebra
Sparse linear algebra includes the fundamental and important operations in various large-
scale scientific computing and real-world applications. There exists performance bottleneck …
scale scientific computing and real-world applications. There exists performance bottleneck …
A self-paced multiple-instance learning framework for co-saliency detection
As an interesting and emerging topic, co-saliency detection aims at simultaneously
extracting common salient objects in a group of images. Traditional co-saliency detection …
extracting common salient objects in a group of images. Traditional co-saliency detection …
A new machine learning ensemble model for class imbalance problem of screening enhanced oil recovery methods
Abstract Enhanced Oil Recovery (EOR) methods have received a lot of attention today due
to the increase in global oil demand and the reduction of oil production capacity from natural …
to the increase in global oil demand and the reduction of oil production capacity from natural …
Class imbalance learning using UnderBagging based kernelized extreme learning machine
BS Raghuwanshi, S Shukla - Neurocomputing, 2019 - Elsevier
Many real-life problems can be described as imbalanced classification problems, where the
number of samples belonging to one of the classes is heavily outnumbered than the …
number of samples belonging to one of the classes is heavily outnumbered than the …