Partial Label Optimal Margin Distribution Machine

2021 ◽  
Author(s):  
TianCheng Wang ◽  
Feng Hu ◽  
Xin Liu ◽  
WeiBin Deng ◽  
SaiSai Li
Keyword(s):  
Margin Distribution

Margin Distribution Analysis

2021 ◽  
pp. 1-13
Author(s):  
Jun Wang ◽  
Zhi-Hua Zhou
Keyword(s):  
Distribution Analysis ◽  
Margin Distribution

MULFE: Multi-Label Learning via Label-Specific Feature Space Ensemble

2021 ◽  
Vol 16 (1) ◽  
pp. 1-24
Author(s):  
Yaojin Lin ◽  
Qinghua Hu ◽  
Jinghua Liu ◽  
Xingquan Zhu ◽  
Xindong Wu
Keyword(s):  
Empirical Studies ◽  
Feature Space ◽  
Training Data ◽  
Data Sets ◽  
Learning Framework ◽  
Feature Spaces ◽  
Public Data ◽  
Margin Distribution ◽  
Label Correlations ◽  
Label Correlation

In multi-label learning, label correlations commonly exist in the data. Such correlation not only provides useful information, but also imposes significant challenges for multi-label learning. Recently, label-specific feature embedding has been proposed to explore label-specific features from the training data, and uses feature highly customized to the multi-label set for learning. While such feature embedding methods have demonstrated good performance, the creation of the feature embedding space is only based on a single label, without considering label correlations in the data. In this article, we propose to combine multiple label-specific feature spaces, using label correlation, for multi-label learning. The proposed algorithm, mu lti- l abel-specific f eature space e nsemble (MULFE), takes consideration label-specific features, label correlation, and weighted ensemble principle to form a learning framework. By conducting clustering analysis on each label’s negative and positive instances, MULFE first creates features customized to each label. After that, MULFE utilizes the label correlation to optimize the margin distribution of the base classifiers which are induced by the related label-specific feature spaces. By combining multiple label-specific features, label correlation based weighting, and ensemble learning, MULFE achieves maximum margin multi-label classification goal through the underlying optimization framework. Empirical studies on 10 public data sets manifest the effectiveness of MULFE.

Least squares large margin distribution machine for regression

2021 ◽  
Author(s):  
Umesh Gupta ◽  
Deepak Gupta
Keyword(s):  
Least Squares ◽  
Large Margin ◽  
Margin Distribution

scholarly journals A Yes-Associated Protein (YAP) and Insulin-Like Growth Factor 1 Receptor (IGF-1R) Signaling Loop Is Involved in Sorafenib Resistance in Hepatocellular Carcinoma

Cancers ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 3812
Author(s):  
Mai-Huong T. Ngo ◽  
Sue-Wei Peng ◽  
Yung-Che Kuo ◽  
Chun-Yen Lin ◽  
Ming-Heng Wu ◽  
...  
Keyword(s):  
Nuclear Translocation ◽  
Combination Index ◽  
Specific Inhibitor ◽  
In Vivo Model ◽  
Mrna Levels ◽  
Margin Distribution ◽  
Related Proteins ◽  
Emt Markers

The role of a YAP-IGF-1R signaling loop in HCC resistance to sorafenib remains unknown. Method: Sorafenib-resistant cells were generated by treating naïve cells (HepG2215 and Hep3B) with sorafenib. Different cancer cell lines from databases were analyzed through the ONCOMINE web server. BIOSTORM–LIHC patient tissues (46 nonresponders and 21 responders to sorafenib) were used to compare YAP mRNA levels. The HepG2215_R-derived xenograft in SCID mice was used as an in vivo model. HCC tissues from a patient with sorafenib failure were used to examine differences in YAP and IGF-R signaling. Results: Positive associations exist among the levels of YAP, IGF-1R, and EMT markers in HCC tissues and the levels of these proteins increased with sorafenib failure, with a trend of tumor-margin distribution in vivo. Blocking YAP downregulated IGF-1R signaling-related proteins, while IGF-1/2 treatment enhanced the nuclear translocation of YAP in HCC cells through PI3K-mTOR regulation. The combination of YAP-specific inhibitor verteporfin (VP) and sorafenib effectively decreased cell viability in a synergistic manner, evidenced by the combination index (CI). Conclusion: A YAP-IGF-1R signaling loop may play a role in HCC sorafenib resistance and could provide novel potential targets for combination therapy with sorafenib to overcome drug resistance in HCC.

scholarly journals Unconstrained large margin distribution machines

2017 ◽  
Vol 98 ◽  
pp. 96-102 ◽  
Author(s):  
Shigeo Abe
Keyword(s):  
Large Margin ◽  
Margin Distribution

scholarly journals Durian Marketing Efficiency in Durian Tourism Village in Sumber Agung

2018 ◽  
Vol 6 (2) ◽  
pp. 68
Author(s):  
Dita Wulandari ◽  
Rommy Qurniati ◽  
Susni Herwanti
Keyword(s):  
Market Structure ◽  
Marketing Channels ◽  
Market Behavior ◽  
Margin Distribution ◽  
Marketing Efficiency ◽  
Final Consumer

Regional Forestry Office of Lampung Province has declared Sumber Agung Village as a Durian Tourism Village. In order to encourage the durian tourism village as a center of durian marketing, it is necessary to conduct a research which aim to know the efficiency of durian marketing to support the durian tourism village. The analysis used in this study were including the analysis of marketing channels, market structure, market behavior and marketing margin. The research showed that there were 4 durian marketing channels in Sumber Agung Village and the most efficient channel was from farmer through retailer and to final consumer. According to the market structure, market behavior and uneven margin distribution, the durian marketing has not been efficient and tends to be oligopsony. Keywords: durian, efficiency, marketing, tourism village.

Kernel modified optimal margin distribution machine for imbalanced data classification

2019 ◽  
Vol 125 ◽  
pp. 325-332 ◽  
Author(s):  
Xiaogang Zhang ◽  
Dingxiang Wang ◽  
Yicong Zhou ◽  
Hua Chen ◽  
Fanyong Cheng ◽  
...  
Keyword(s):  
Imbalanced Data ◽  
Data Classification ◽  
Imbalanced Data Classification ◽  
Margin Distribution

Revisiting transductive support vector machines with margin distribution embedding

2018 ◽  
Vol 152 ◽  
pp. 200-214 ◽  
Author(s):  
Yanchao Li ◽  
Yongli Wang ◽  
Cheng Bi ◽  
Xiaohui Jiang
Keyword(s):  
Support Vector Machines ◽  
Support Vector ◽  
Margin Distribution ◽  
Vector Machines
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