A Maximum Entropy Model for Large-Scale Portfolio Optimization

2008 ◽  
Author(s):  
Yuxi Jiang ◽  
Suyan He ◽  
Xingsi Li
Keyword(s):  
Portfolio Optimization ◽  
Maximum Entropy ◽  
Large Scale ◽  
Maximum Entropy Model ◽  
Entropy Model

scholarly journals Pairwise maximum entropy model explains the role of white matter structure in shaping emergent co-activation states

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Arian Ashourvan ◽  
Preya Shah ◽  
Adam Pines ◽  
Shi Gu ◽  
Christopher W. Lynn ◽  
...  
Keyword(s):  
White Matter ◽  
Maximum Entropy ◽  
Large Scale ◽  
Structural Connectivity ◽  
Quantitative Relationship ◽  
Brain Regions ◽  
Maximum Entropy Model ◽  
Entropy Model ◽  
Activation Patterns ◽  
Co Activation

AbstractA major challenge in neuroscience is determining a quantitative relationship between the brain’s white matter structural connectivity and emergent activity. We seek to uncover the intrinsic relationship among brain regions fundamental to their functional activity by constructing a pairwise maximum entropy model (MEM) of the inter-ictal activation patterns of five patients with medically refractory epilepsy over an average of ~14 hours of band-passed intracranial EEG (iEEG) recordings per patient. We find that the pairwise MEM accurately predicts iEEG electrodes’ activation patterns’ probability and their pairwise correlations. We demonstrate that the estimated pairwise MEM’s interaction weights predict structural connectivity and its strength over several frequencies significantly beyond what is expected based solely on sampled regions’ distance in most patients. Together, the pairwise MEM offers a framework for explaining iEEG functional connectivity and provides insight into how the brain’s structural connectome gives rise to large-scale activation patterns by promoting co-activation between connected structures.

scholarly journals Clothing Product Reviews Mining Based on Machine Learning

2015 ◽  
Vol 11 (9) ◽  
pp. 71
Author(s):  
Qing Hong ◽  
Peifei Feng ◽  
Zhichao Cheng
Keyword(s):  
Machine Learning ◽  
Maximum Entropy ◽  
Classification Accuracy ◽  
Large Scale ◽  
Classification Problem ◽  
Product Reviews ◽  
Maximum Entropy Model ◽  
Entropy Model ◽  
In The Beginning ◽  
Multi Classification

This paper used the method of machine learning to study clothing product reviews classification based on big enterprise data. Taking Taobao clothing reviews as the object, it firstly excavated review themes from reviews corpus by association rules, and then searched review themes related to the categories by a method of mutual information to enrich the review themes. In the process of building classification models, commonly used SVM classifiers were studied in the beginning. After training and verification of a large amount of data, the classification accuracy reached 90.597%. In order to further improve the classification accuracy, the maximum entropy model was built by adopting the maximum entropy algorithm, on the basis of the same review themes. After repeated experiments and optimization in a large-scale of clothing product reviews, the classification accuracy reached up to 93.035% finally. Compared with SVM classification algorithm, the accuracy of maximum entropy in the clothing product reviews classification is higher. This paper verified the effectiveness of maximum entropy model on comment text multi-classification problem, and the maximum entropy model has practical values in electronic business.

Video segmentation using Maximum Entropy Model

2005 ◽  
Vol 6 (S1) ◽  
pp. 47-52
Author(s):  
Li-juan Qin ◽  
Yue-ting Zhuang ◽  
Yun-he Pan ◽  
Fei Wu
Keyword(s):  
Maximum Entropy ◽  
Video Segmentation ◽  
Maximum Entropy Model ◽  
Entropy Model
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