A sequential multitask learning algorithm for pattern recognition

2012 ◽  
Author(s):  
Tomoyasu Takata ◽  
Daisuke Higuchi ◽  
Seiichi Ozawa
Keyword(s):  
Pattern Recognition ◽  
Learning Algorithm ◽  
Multitask Learning

scholarly journals Remote Judgment Method of Painting Image Style Plagiarism Based on Wireless Network Multitask Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Zhijun Wang
Keyword(s):  
Wireless Network ◽  
Kernel Function ◽  
Digital Image ◽  
Learning Algorithm ◽  
Deep Level ◽  
Multitask Learning ◽  
Experimental Results ◽  
Spatial Consistency ◽  
Discrimination Rate ◽  
Edge Sampling

Since the artistry of the work cannot be accurately described, the identification of reproducible plagiarism is more difficult. The identification of reproducible plagiarism of digital image works requires in-depth research on the artistry of artistic works. In this paper, a remote judgment method for plagiarism of painting image style based on wireless network multitask learning is proposed. According to this new method, the uncertainty of painting image samples is removed based on multitask learning algorithm edge sampling. The deep-level details of the painting image are extracted through the multitask classification kernel function, and most of the pixels in the image are eliminated. When the clustering density is greater than the judgment threshold, it can be considered that the two images have spatial consistency. It can also be judged based on this that the two images are similar, that is, there is plagiarism in the painting. The experimental results show that the discrimination rate is always close to 100%, the misjudgment rate of plagiarism of painting images has been reduced, and the various indicators in the discrimination process are the lowest, which fully shows that a very satisfactory discrimination result can be obtained.

scholarly journals Confidence Weighted Multitask Learning

2019 ◽  
Vol 33 ◽  
pp. 5636-5643
Author(s):  
Peng Yang ◽  
Peilin Zhao ◽  
Jiayu Zhou ◽  
Xin Gao
Keyword(s):  
Gaussian Distribution ◽  
Learning Process ◽  
Learning Algorithm ◽  
Multitask Learning ◽  
Local Component ◽  
Label Prediction ◽  
Learning Efficacy ◽  
Regret Bounds ◽  
The Mean ◽  
Theoretical Results

Traditional online multitask learning only utilizes the firstorder information of the datastream. To remedy this issue, we propose a confidence weighted multitask learning algorithm, which maintains a Gaussian distribution over each task model to guide online learning process. The mean (covariance) of the Gaussian Distribution is a sum of a local component and a global component that is shared among all the tasks. In addition, this paper also addresses the challenge of active learning on the online multitask setting. Instead of requiring labels of all the instances, the proposed algorithm determines whether the learner should acquire a label by considering the confidence from its related tasks over label prediction. Theoretical results show the regret bounds can be significantly reduced. Empirical results demonstrate that the proposed algorithm is able to achieve promising learning efficacy, while simultaneously minimizing the labeling cost.

scholarly journals Lung and Tumor Characterization in the Machine Learning Era

2021 ◽  
Vol 10 (5) ◽  
pp. 131-134
Author(s):  
R. Subalakshmi ◽  
G. Baskar
Keyword(s):  
Machine Learning ◽  
Feature Fusion ◽  
Learning Algorithm ◽  
Multitask Learning ◽  
Machine Learning Algorithm ◽  
Tumor Characterization ◽  
Feature Representations ◽  
Aided Diagnosis ◽  
Radiology Image

Danger characterization of tumors from radiology image container to be much precise and quicker with computer aided diagnosis (CAD) implements. Tumor portrayal via such devices can likewise empower non-intrusive prognosis, and foster personalized, and treatment arranging as a piece of accuracy medication. In this study , in cooperation machine learning algorithm strategies to better tumor characterization. Our methodological analysis depends on directed erudition for which we exhibit critical increases with machine learning algorithm, particularly by exploitation a 3D Convolutional Neural Network and Transfer Learning. Disturbed by the radiologists' understandings of the outputs, we at that point tell the best way to fuse task subordinate feature representations into a CAD framework by means of a diagram regularized inadequate MultiTask Learning (MTL) system with the help of feature fusion.

scholarly journals Relaxometric learning: a pattern recognition method for T2 relaxation curves based on machine learning supported by an analytical framework

2020 ◽  
Author(s):  
Yasuhiro Date ◽  
Feifei Wei ◽  
Yuuri Tsuboi ◽  
Kengo Ito ◽  
Kenji Sakata ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Pattern Recognition ◽  
Geographical Origin ◽  
Learning Algorithm ◽  
Analytical Framework ◽  
Classification Performance ◽  
Optimization Approach ◽  
Chemical Research ◽  
Relaxation Curves

Abstract Nuclear magnetic resonance (NMR)-based relaxometry is widely used in various fields of research because of its advantages such as simple sample preparation, easy handling, and relatively low cost compared with metabolomics approaches. However, there have been no reports on the application of the T2 relaxation curves in metabolomics studies involving the evaluation of metabolic mixtures, such as geographical origin determination and feature extraction by pattern recognition and data mining. In this study, we describe a data mining method for relaxometric data (i.e., relaxometric learning). This method is based on a machine learning algorithm supported by the analytical framework optimized for the relaxation curve analyses. In the analytical framework, we incorporated a variable optimization approach and bootstrap resampling-based matrixing to enhance the classification performance and balance the sample size between groups, respectively. The relaxometric learning enabled the extraction of features related to the physical properties of fish muscle and the determination of the geographical origin of the fish by improving the classification performance. Our results suggest that relaxometric learning is a powerful and versatile alternative to conventional metabolomics approaches for evaluating fleshiness of chemical mixtures in food and for other biological and chemical research requiring a nondestructive, cost-effective, and time-saving method.

A Hybrid Higher Order Neural Structure for Pattern Recognition

2013 ◽  
pp. 364-387 ◽  
Author(s):  
Mehdi Fallahnezhad ◽  
Salman Zaferanlouei
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Structure Learning ◽  
Learning Algorithm ◽  
Hybrid Structure ◽  
Higher Order ◽  
Superior Performance ◽  
Data Sets ◽  
Neural Structure ◽  
Higher Order Neural Networks

Considering high order correlations of selected features next to the raw features of input can facilitate target pattern recognition. In artificial intelligence, this is being addressed by Higher Order Neural Networks (HONNs). In general, HONN structures provide superior specifications (e.g. resolving the dilemma of choosing the number of neurons and layers of networks, better fitting specs, quicker, and open-box specificity) to traditional neural networks. This chapter introduces a hybrid structure of higher order neural networks, which can be generally applied in various branches of pattern recognition. Structure, learning algorithm, and network configuration are introduced, and structure is applied either as classifier (where is called HHONC) to different benchmark statistical data sets or as functional behavior approximation (where is called HHONN) to a heat and mass transfer dilemma. In each structure, results are compared with previous studies, which show its superior performance next to other mentioned advantages.

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