2-Slave Dual Decomposition for Generalized Higher Order CRFs

Power Function Distribution Characterized by Dual Generalized Order Statistics

The dual generalized order statistics is a unified model which contains the well known decreasingly ordered random data such as (reversed ordered) order statistics and lower record values. In the present paper, some characterization results on the power function distribution based on the properties of dual generalized order statistics are provided. The results are proved without any rest...

Phase Tuning in Synchronization of Nonlinear Master-slave Oscillating Systems Using Decomposition Method

Thermoelastic Response of a Rotating Hollow Cylinder Based on Generalized Model with Higher Order Derivatives and Phase-Lags

Generalized thermoelastic models have been developed with the aim of eliminating the contradiction in the infinite velocity of heat propagation inherent in the classical dynamical coupled thermoelasticity theory. In these generalized models, the basic equations include thermal relaxation times and they are of hyperbolic type. Furthermore, Tzou established the dual-phase-lag heat conduction theo...

Speed-Accuracy Tradeoffs in Tagging with Variable-Order CRFs and Structured Sparsity

We propose a method for learning the structure of variable-order CRFs, a more flexible variant of higher-order linear-chain CRFs. Variableorder CRFs achieve faster inference by including features for only some of the tag ngrams. Our learning method discovers the useful higher-order features at the same time as it trains their weights, by maximizing an objective that combines log-likelihood with...

Generalized Conditional Random Fields

Conditional random fields (CRFs) have shown significant improvements over existing methods for structured data labeling. However independence assumptions made by CRFs decrease the usability of the models produced. Currently, CRF models accomodate dependence between only adjacent labels. Generalized CRFs proposed in this study relaxes assumptions of CRFs without reducing tractability of inferenc...