Search Results for author:
Found 4 papers, 1 papers with code
Regularizing Model Complexity and Label Structure for Multi-Label Text Classification
Multi-label text classifiers need to be carefully regularized to prevent the severe over-fitting in the high dimensional space, and also need to take into account label dependencies in order to make accurate predictions under uncertainty.
Ranking-Based Autoencoder for Extreme Multi-label Classification
Extreme Multi-label classification (XML) is an important yet challenging machine learning task, that assigns to each instance its most relevant candidate labels from an extremely large label collection, where the numbers of labels, features and instances could be thousands or millions.
Ideology Detection for Twitter Users with Heterogeneous Types of Links
The problem of ideology detection is to study the latent (political) placement for people, which is traditionally studied on politicians according to their voting behaviors.
Social and Information Networks
Understanding the drivers of sustainable land expansion using a patch-level simulation model: A case study in Wuhan, China
The change complexity lies in the detailed scale of high granularity data, and in the geometric units used to simulate the change.
Computers and Society
