Results 241 to 250 of about 1,256,985 (257)

Multi-Instance Nonparallel Tube Learning

IEEE Transactions on Neural Networks and Learning Systems
In multi-instance nonparallel plane learning (NPL), the training set is comprised of bags of instances and the nonparallel planes are trained to classify the bags. Most of the existing multi-instance NPL methods are proposed based on a twin support vector machine (TWSVM).
Yanshan Xiao, Bo Liu, Zhifeng Hao
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Diversified dictionaries for multi-instance learning

Pattern Recognition, 2017
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Qiao, Maoying, Liu, Liu, Yu, Jun, Xu, Chang, Tao, Dacheng   +4 more
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Multi-instance Learning for Bankruptcy Prediction

2008 Third International Conference on Convergence and Hybrid Information Technology, 2008
Forecast of corporate bankruptcy is a phenomenon of increasing interest to investors/creditors, borrowing firms and governments. Early identification of firms' impending failure is very desirable. The scope of this paper is to investigate the efficiency of multi-instance learning in such an environment.
Sotiris Kotsiantis, Dimitris Kanellopoulos   +1 more
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Bag-level active multi-instance learning

2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), 2011
Multi-Instance Learning (MIL) is a special scheme in machine learning. In recent research it is successfully applied in text classification problem. However, MIL is naturally semi-supervised since the instances labels are unknown for positive bags, which would cut down the accuracy of predictors, or require more computational cost to reduce uncertainty,
Jian Fu, Jian Yin
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Cross-validated smooth multi-instance learning

2017 International Joint Conference on Neural Networks (IJCNN), 2017
The problem of object localization in image appear ubiquitously in computer vision applications including image classification, object detection and visual tracking. Recently, it is shown that multiple-instance learning(MIL) which is regarded as the fourth machine learning framework compared with supervised learning, unsupervised learning and reinforce
Dayuan Li, Lin Zhu, Wenzheng Bao, Fei Cheng, Yi Ren, De-Shuang Huang   +5 more
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Multi-instance Learning

2018
The domain of multi-instance learning (MIL) deals with datasets consisting of compound data samples. Instead of representing an observation as an instance described by a single feature vector, each observation (called a bag) corresponds to a set of instances and, consequently, a set of feature vectors.
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Multi-Instance Learning Based Web Mining

Applied Intelligence, 2005
In multi-instance learning, the training set comprises labeled bags that are composed of unlabeled instances, and the task is to predict the labels of unseen bags. In this paper, a web mining problem, i.e. web index recommendation, is investigated from a multi-instance view.
Zhi-Hua Zhou, Kai Jiang, Ming Li
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Learning Sparse Kernel Classifiers for Multi-Instance Classification

IEEE Transactions on Neural Networks and Learning Systems, 2013
We propose a direct approach to learning sparse kernel classifiers for multi-instance (MI) classification to improve efficiency while maintaining predictive accuracy. The proposed method builds on a convex formulation for MI classification by considering the average score of individual instances for bag-level prediction.
Fu, Zhouyu (R16983), Lu, Guojun, Ting, Kaiming, Zhang, Dengsheng   +3 more
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Active Multi-Instance Multi-Label Learning

2016
Multi-instance multi-label learning (MIML) introduced by Zhou and Zhang is a comparatively new framework in machine learning with two special characteristics: Firstly, each instance is represented by a set of feature vectors (a bag of instances), and secondly, bags of instances may belong to many classes (a Multi-Label).
Robert Retz, Friedhelm Schwenker
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A Review of Latest Multi-instance Learning

2020 4th International Conference on Computer Science and Artificial Intelligence, 2020
Due to the application needs of some special scenarios, multi-instance learning problem has been paid more and more attention in recent years. Different from the traditional supervised learning problem, each example in the training set of multi-instance learning is not represented by a single feature vector, but a group of feature vectors, the example ...
Yuan Tian, Wenning Hao, Dawei Jin, Gang Chen, Ao Zou   +4 more
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