发明名称 |
MULTI-TASK LEARNING USING BAYESIAN MODEL WITH ENFORCED SPARSITY AND LEVERAGING OF TASK CORRELATIONS |
摘要 |
Multi-task regression or classification includes optimizing parameters of a Bayesian model representing relationships between D features and P tasks, where D>=1 and P>=1, respective to training data comprising sets of values for the D features annotated with values for the P tasks. The Bayesian model includes a matrix-variate prior having features and tasks dimensions of dimensionality D and P respectively. The matrix-variate prior is partitioned into a plurality of blocks, and the optimizing of parameters of the Bayesian model includes inferring prior distributions for the blocks of the matrix-variate prior that induce sparseness of the plurality of blocks. Values of the P tasks are predicted for a set of input values for the D features using the optimized Bayesian model. The optimizing also includes decomposing the matrix-variate prior into a product of matrices including a matrix of reduced rank in the tasks dimension that encodes correlations between tasks.
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申请公布号 |
US2013151441(A1) |
申请公布日期 |
2013.06.13 |
申请号 |
US201113324060 |
申请日期 |
2011.12.13 |
申请人 |
ARCHAMBEAU CEDRIC;GUO SHENGBO;ZOETER ONNO;ANDREOLI JEAN-MARC;XEROX CORPORATION |
发明人 |
ARCHAMBEAU CEDRIC;GUO SHENGBO;ZOETER ONNO;ANDREOLI JEAN-MARC |
分类号 |
G06F15/18 |
主分类号 |
G06F15/18 |
代理机构 |
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地址 |
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