Journal ArticleDOI
Pattern Recognition and Machine Learning
TLDR
This book covers a broad range of topics for regular factorial designs and presents all of the material in very mathematical fashion and will surely become an invaluable resource for researchers and graduate students doing research in the design of factorial experiments.Abstract:
(2007). Pattern Recognition and Machine Learning. Technometrics: Vol. 49, No. 3, pp. 366-366.read more
Citations
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Proceedings ArticleDOI
Bayesian Unsupervised Topic Segmentation
Jacob Eisenstein,Regina Barzilay +1 more
TL;DR: A novel Bayesian approach to unsupervised topic segmentation is described, showing that lexical cohesion can be placed in a Bayesian context by modeling the words in each topic segment as draws from a multinomial language model associated with the segment; maximizing the observation likelihood in such a model yields a lexically-cohesive segmentation.
Journal ArticleDOI
New Frontiers in Spectral-Spatial Hyperspectral Image Classification: The Latest Advances Based on Mathematical Morphology, Markov Random Fields, Segmentation, Sparse Representation, and Deep Learning
Pedram Ghamisi,Emmanuel Maggiori,Shutao Li,Roberto Souza,Yuliya Tarablaka,Gabriele Moser,Andrea De Giorgi,Leyuan Fang,Yushi Chen,Mingmin Chi,Sebastiano B. Serpico,Jon Atli Benediktsson +11 more
TL;DR: In recent years, airborne and spaceborne hyperspectral imaging systems have advanced in terms of spectral and spatial resolution, which makes the data sets they produce a valuable source for land cover classification.
Journal ArticleDOI
Inference for psychometric functions in the presence of nonstationary behavior
TL;DR: Monte Carlo simulations are used to show that violations of these assumptions can result in underestimation of confidence intervals for parameters of the psychometric function and a simple adjustment of the confidence intervals is presented that corrects for the underestimation almost independently of the number of trials and the particular type of violation.
Rapid dissection and model-based optimization of inducible enhancers in human cells using a massively parallel reporter assay
Alexandre Melnikov,Anand Murugan,Xiaolan Zhang,Tiberiu Tesileanu,Li Wang,Peter Rogov,Soheil Feizi,Andreas Gnirke,Curtis G. Callan,Justin B. Kinney,Manolis Kellis,Eric S. Lander,Tarjei S. Mikkelsen +12 more
Journal ArticleDOI
An empirical study on software defect prediction with a simplified metric set
TL;DR: The experimental results indicate that the choice of training data for defect prediction should depend on the specific requirement of accuracy and the minimum metric subset can be identified to facilitate the procedure of general defect prediction with acceptable loss of prediction precision in practice.