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Found 11 papers, 0 papers with code
Efficient stereo matching on embedded GPUs with zero-means cross correlation
By combining this technique with the domain transformation (DT) algorithm, our system show real-time processing speed of 32 fps, on a Jetson Tx2 GPU for 1, 280x384 pixel images with a maximum disparity of 128.
Population structure-learned classifier for high-dimension low-sample-size class-imbalanced problem
The Classification on high-dimension low-sample-size data (HDLSS) is a challenging problem and it is common to have class-imbalanced data in most application fields.
The classification for High-dimension low-sample size data
In this paper, we propose a novel classification criterion on HDLSS, tolerance similarity, which emphasizes the maximization of within-class variance on the premise of class separability.
Targeted Advertising Based on Browsing History
Audience interest, demography, purchase behavior and other possible classifications are ex- tremely important factors to be carefully studied in a targeting campaign.
A Novel Progressive Multi-label Classifier for Classincremental Data
In this paper, a progressive learning algorithm for multi-label classification to learn new labels while retaining the knowledge of previous labels is designed.
An Online Universal Classifier for Binary, Multi-class and Multi-label Classification
Several classifiers are developed for binary, multi-class and multi-label classification problems, but there are no classifiers available in the literature capable of performing all three types of classification.
A Novel Progressive Learning Technique for Multi-class Classification
In this paper, a progressive learning technique for multi-class classification is proposed.
A novel online multi-label classifier for high-speed streaming data applications
In this paper, a high-speed online neural network classifier based on extreme learning machines for multi-label classification is proposed.
A Novel Online Real-time Classifier for Multi-label Data Streams
In this paper, a novel extreme learning machine based online multi-label classifier for real-time data streams is proposed.
A High Speed Multi-label Classifier based on Extreme Learning Machines
In this paper a high speed neural network classifier based on extreme learning machines for multi-label classification problem is proposed and dis-cussed.
Multi-Label Classification Method Based on Extreme Learning Machines
The comparative results shows that the proposed Extreme Learning Machine based multi-label classification technique is a better alternative than the existing state of the art methods for multi-label problems.
