We describe Akamon, an open source toolkit for tree and forest-based statistical machine translation (Liu et al., 2006; Mi et al., 2008; Mi and Huang, 2008). Akamon implements all...
We present the first PAC bounds for learning parameters of Conditional Random Fields [12] with general structures over discrete and real-valued variables. Our bounds apply to com...
We propose a framework for large scale learning and annotation of structured models. The system interleaves interactive labeling (where the current model is used to semiautomate t...
We develop a new algorithm to segment the hippocampus from MR images. Our method uses a new classifier ensemble algorithm to correct segmentation errors produced by a multi-atlas...
Hongzhi Wang, Jung Wook Suh, Sandhitsu R. Das, Mur...
This paper describes subspace constrained feature space maximum likelihood linear regression (FMLLR) for rapid adaptation. The test speaker’s FMLLR rotation matrix is decomposed...
Iterative decoding is considered in this paper from an optimization point of view. Starting from the optimal maximum likelihood decoding, a (tractable) approximate criterion is de...
The maximum likelihood estimate of the impulse response of a frequency-selective channel in the presence of phase noise and I/Q imbalance is derived. The complexity of the joint es...
Abstract. Linear inverse problems with uncertain measurement matrices appear in many different applications. One of the standard techniques for solving such problems is the total l...
Recent research has seen the proposal of several new inductive principles designed specifically to avoid the problems associated with maximum likelihood learning in models with in...
Benjamin Marlin, Kevin Swersky, Bo Chen, Nando de ...
We present a region-based active contour approach to segmenting masses in digital mammograms. The algorithm developed in a Maximum Likelihood approach is based on the calculation o...