Sample Training Based Wildfire Segmentation by 2D Histogramθ-Division with Minimum Error

Sample Training Based Wildfire Segmentation by 2D Histogram θ-Division with Minimum Error

A novel wildfire segmentation algorithm is proposed with the help of sample training based 2D histogram θ-division and minimum error. Based on minimum error principle and 2D color histogram, the θ-division methods were presented recently, but application of prior knowledge on them has not been explored. For the specific problem of wildfire segmentation, we collect sample images with manually la...

Minimum boundary error training for automatic phonetic segmentation

Annotated speech corpora are indispensable to various areas of speech research. In this paper, we present a novel discriminative training approach for HMM-based automatic phonetic segmentation. The objective of the proposed minimum boundary error (MBE) discriminative training approach is to minimize the expected boundary errors over a set of phonetic alignments represented as a phonetic lattice...

Minimum Error Rate Training Semiring

Modern Statistical Machine Translation (SMT) systems make their decisions based on multiple information sources, which assess various aspects of the match between a source sentence and its possible translation(s). Tuning a SMT system consists in finding the right balance between these sources so as to produce the best possible output, and is usually achieved through Minimum Error Rate Training ...

Stabilizing Minimum Error Rate Training

The most commonly used method for training feature weights in statistical machine translation (SMT) systems is Och’s minimum error rate training (MERT) procedure. A well-known problemwith Och’s procedure is that it tends to be sensitive to small changes in the system, particularly when the number of features is large. In this paper, we quantify the stability of Och’s procedure by supplying diff...

Fast Minimum Training Error Discretization