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Fast ADMM Algorithm for Distributed Optimization with Adaptive Penalty
It is shown that the proposed method accelerates the speed of convergence of the ADMM by automatically deciding the constraint penalty needed for parameter consensus in each iteration, and also proposes an extension of the method that adaptively determines the maximum number of iterations to update the penalty. Expand
Relative spatial features for image memorability
The Weighted Object Area (WOA) that jointly considers the location and size of objects and the Relative Area Rank (RAR) that captures the relative unusualness of the size of Objects are proposed and empirically demonstrate their useful correlation with the image memorability. Expand
Mass Lesions Classification in Digital Mammography using Optimal Subset of BI-RADS and Gray Level Features
Computer-aided diagnosis of mass lesions in Digital Database for Screening Mammography is investigated using a recently developed SVM based on recursive feature elimination (SVM-RFE) as the classification technique, and results indicate that using only a subset of the available set of features facilitates increased computer- aided diagnosis accuracy. Expand
Mutual information-based SVM-RFE for diagnostic classification of digitized mammograms
A mutual information-based Support Vector Machine Recursive Feature Elimination (SVM-RFE) as the classification method with feature selection in this paper and results indicate that the proposed method outperforms other SVM and SVM- RFE-based methods. Expand
k-Top Scoring Pair Algorithm for feature selection in SVM with applications to microarray data classification
Results on ten public domain microarray data indicated that TSP family classifiers serve as good feature selection schemes which may be combined effectively with other classification methods. Expand
Sentiment Flow for Video Interestingness Prediction
This work extends a recent pilot study on the video interestingness prediction by using a mid-level representation of sentiment (emotion) sequence and evaluates the proposed framework on three datasets including the datasets proposed by the pilot study and shows that the result effectively verifies a promising utility of the approach. Expand
The Role of Data-driven Priors in Multi-agent Crowd Trajectory Estimation
This paper proposes a novel framework composed of data-driven priors (local, global or combined) and an efficient optimization strategy for multi-agent trajectory interpolation, enabling elimination of costly pairwise collision constraints and resulting in reduced computational complexity and often improved estimation. Expand
TCNJ-CS@MediaEval 2017 Emotional Impact of Movie Task
The approaches for the MediaEval Emotional Impact of Movies Task employed features from image frames and audio signal and introduced a new feature using exponential decay of the initially predicted emotion labels to computationally model lingering effect. Expand
TCNJ-CS@MediaEval 2017 Predicting Media Interestingness Task
The standard kernel fusion technique was applied to combine features and the ranking support vector machine was used to learn the classification model, and no extra data was introduced to train the model. Expand
AdaBoost-based multiple SVM-RFE for classification of mammograms in DDSM
  • Sejong Yoon, Saejoon Kim
  • Computer Science, Medicine
  • IEEE International Conference on Bioinformatics…
  • 3 November 2009
A feature selection method based on multiple support vector machine recursive feature elimination (MSVM-RFE) using boosting outperformed or was at least competitive to all others when selecting from 22 features. Expand