We address the problem of predicting a word from previous words in a sample of text. In particular, we discuss n-gram models based on classes of words. We also discuss several statistical algorithms for assigning words to classes based on the frequency of their co-occurrence with other words. We find that we are able to Extract classes that have the flavor of either syntactically based groupings or semantically based groupings, depending on the nature of the underlying statistics.
標簽： predicting particular previous address
上傳時間： 2016-12-26
上傳用戶：xfbs821
Eclipse+Web開發從入門到精通 These files contain all of the code listings in Java: The Complete Reference, J2SE 5 Edition The source code is organized into files by chapter. For example, the file Chap7.code contains the programs shown in Chapter 7. Within each chapter file, the listings are stored in the same order as they appear in the book. Simply edit the appropriate file to Extract the listing in which you are interested.
標簽： Complete Referenc listings Eclipse
上傳時間： 2017-01-20
上傳用戶：as275944189
Requirement ===================================================================================== python 2.4+ wxPython 2.6+ Unicode Version Installation ===================================================================================== Directly Extract the tarbar into a empty directory or overwrite the old directory, that s ok. Run ===================================================================================== Execute: python ulipad.pyw or python ulipad.py
標簽： Requirement
上傳時間： 2017-02-01
上傳用戶：ruan2570406
These files contain all of the code listings in Java 2: The Complete Reference The source code is organized into files by chapter. Within each chapter file, the listings are stored in the same order as they appear in the book. Simply edit the appropriate file to Extract the listing in which you are interested. The code for Scrabblet is in its own ZIP file, called CHAP32.ZIP.
標簽： The Reference Complete listings
上傳時間： 2013-11-29
上傳用戶：3到15
This article presents GISCoordinate.java - a class that allows you to represent a GIS coordinate in your JAVA code in decimal degrees (38.4443, e.g. 122.33433) , minute degrees (33 44 22E, 122 33 44N), or radian degrees. Also, you can use this class to manipulate the coordinate, moving it around the globe by giving it distances in feet and direction of travel. You can then Extract the new coordinate that is calculated after the travel.
標簽： GISCoordinate coordinate represent presents
上傳時間： 2013-12-02
上傳用戶：wangchong
HDFDUMP and BRFDUMP are utility programs developed for use with MISR data files. HDFDUMP will Extract data from a MISR file in the HDF-EOS grid format (MISR Level 1B2 and Level 2 files) and writes unformatted binary files. BRFDUMP calculates radiances and bidirectional reflectance factors (BRF) from MISR Level 1B2 files and creates unformatted binary files.
標簽： HDFDUMP developed programs BRFDUMP
上傳時間： 2017-04-02
上傳用戶：yy541071797
This document explains how to read the RADARSAT data CD provided with the book "Digital Processing of Synthetic Aperture Data" by Ian Cumming and Frank Wong, Artech House, 2005. On this web site, programs are provided in MATLAB to read the data and parameters from the CD. The structure of the programs is outlined in Figure 1. There are two main programs and one function that are used to Extract
標簽： Processing the RADARSAT document
上傳時間： 2013-12-16
上傳用戶：561596
This file is a function under matlab which allow to read, play and plot audio signals from wav file. We can also Extract the sampling frequency and coding bit number
標簽： file function signals matlab
上傳時間： 2014-01-25
上傳用戶：diets
This simulation script set allows for an OFDM transmission to be simulated. Imagetx.m generates the OFDM signal, saving it as a windows WAV file. This allows the OFDM signal to be played out a sound card and recorded back. Imagerx.m decodes the WAV to Extract the data.
標簽： transmission simulation generates simulated
上傳時間： 2013-12-24
上傳用戶：希醬大魔王
This paper presents a Hidden Markov Model (HMM)-based speech enhancement method, aiming at reducing non-stationary noise from speech signals. The system is based on the assumption that the speech and the noise are additive and uncorrelated. Cepstral features are used to Extract statistical information from both the speech and the noise. A-priori statistical information is collected from long training sequences into ergodic hidden Markov models. Given the ergodic models for the speech and the noise, a compensated speech-noise model is created by means of parallel model combination, using a log-normal approximation. During the compensation, the mean of every mixture in the speech and noise model is stored. The stored means are then used in the enhancement process to create the most likely speech and noise power spectral distributions using the forward algorithm combined with mixture probability. The distributions are used to generate a Wiener filter for every observation. The paper includes a performance evaluation of the speech enhancer for stationary as well as non-stationary noise environment.
標簽： Telecommunications Processing Signal for
上傳時間： 2020-06-01
上傳用戶：shancjb
