<delect id="46wmg"></delect> Context-Sensitive Semantic smoothing for the Language Modeling Approach to Genomic IR(上下文敏感的語義平滑的語言建模方法)
標簽： Context-Sensitive smoothing Approach Semantic
上傳時間： 2015-11-04
上傳用戶：pkkkkp
measure through the cross-entropy of test data. In addition, we introduce two novel smoothing techniques, one a variation of Jelinek-Mercer smoothing and one a very simple linear interpolation technique, both of which outperform existing methods.
標簽： cross-entropy introduce smoothing addition
上傳時間： 2014-01-06
上傳用戶：qilin
state of art language modeling methods: An Empirical Study of smoothing Techniques for Language Modeling.pdf BLEU, a Method for Automatic Evaluation of Machine Translation.pdf Class-based n-gram models of natural language.pdf Distributed Language Modeling for N-best List Re-ranking.pdf Distributed Word Clustering for Large Scale Class-Based Language Modeling in.pdf
標簽： Techniques Empirical smoothing Language
上傳時間： 2016-12-26
上傳用戶：zhuoying119
Author:Eubank year:(1999)Name:Nonparametric Regression and Spline smoothing second edition
標簽： Nonparametric Regression smoothing edition
上傳時間： 2017-04-18
上傳用戶：hj_18
Interactive smoothing for your own data, with sliders to control derivative order, smooth width, and scale expansion.
標簽： Interactive derivative smoothing control
上傳時間： 2014-11-29
上傳用戶：lanjisu111
Interactive smoothing for time-series signals, with sliders that allow you to adjust the smoothing parameters continuously while observing the effect on your signal dynamically. Run SmoothSliderTest to see how it works.
標簽： smoothing Interactive time-series signals
上傳時間： 2014-12-07
上傳用戶：xinyuzhiqiwuwu
smoothing of images
上傳時間： 2017-04-29
上傳用戶：yepeng139
Linera filtr (gaussian smoothing)
標簽： smoothing gaussian Linera filtr
上傳時間： 2014-01-06
上傳用戶：lepoke
In this article, we present an overview of methods for sequential simulation from posterior distributions. These methods are of particular interest in Bayesian filtering for discrete time dynamic models that are typically nonlinear and non-Gaussian. A general importance sampling framework is developed that unifies many of the methods which have been proposed over the last few decades in several different scientific disciplines. Novel extensions to the existing methods are also proposed.We showin particular how to incorporate local linearisation methods similar to those which have previously been employed in the deterministic filtering literature these lead to very effective importance distributions. Furthermore we describe a method which uses Rao-Blackwellisation in order to take advantage of the analytic structure present in some important classes of state-space models. In a final section we develop algorithms for prediction, smoothing and evaluation of the likelihood in dynamic models.
標簽： sequential simulation posterior overview
上傳時間： 2015-12-31
上傳用戶：225588
srand[getpid[]] /* initialize some of the memory */ memset[heightmap, 0, MAPSIZE*MAPSIZE] memset[vpage, 0, RENDERWIDTH * RENDERHEIGHT] printf["Creating dx d fractal terrain\n", MAPSIZE, MAPSIZE] heightmap[0] = [rand[] 128] + 64 // initialize starting point on map CreateFractalMap[0, 0, MAPSIZE, MAPSIZE] printf["smoothing terrain\n"] for [i = 0 i < 5 i++] SmoothMap[] MakeColorMap[]
標簽： MAPSIZE initialize heightmap getpid
上傳時間： 2014-01-04
上傳用戶：ainimao
