JavaTM 2 Platform Standard Edition 5.0 API 的規(guī)范
標(biāo)簽： Platform Standard Edition JavaTM
上傳時(shí)間： 2016-10-28
上傳用戶：李彥東
Multicast Algorithms for Multi-Channel Wireless Mesh Networks
標(biāo)簽： Multi-Channel Algorithms Multicast Wireless
上傳時(shí)間： 2014-01-12
上傳用戶：as275944189
a Java program that reads in the following values from the standard input device (i.e. Keyboard) and writes its result on the standard output device (i.e. Console/Monitor): Inputs: A: the loan amount in dollars and cents (e.g. 150000.00). r: the net annual interest rate, expressed as an integer (e.g. 10 which means 10％) Y: the number of whole remaining years (for repayment) M: the number of remaining months Output: The program should calculate and output the amount of monthly repayments in dollars and cents as single value (e.g. $840.55)
標(biāo)簽： i.e. following the Keyboard
上傳時(shí)間： 2013-12-15
上傳用戶：米卡
im2dat.m is used to convert images to data which can be plotted using the standard MATLAB functions. This is very handy if you have plots on hardcopy and you want to convert them into data that MATLAB can use. The scanned image can be analysed by this function and the output will allow you to perform any calculation/manipulations that MATLAB can perform, e.g. curve fitting.
標(biāo)簽： functions standard convert plotted
上傳時(shí)間： 2014-12-07
上傳用戶：gdgzhym
IEEE standard Verilog HDL1364-2001.pdf Verilog 學(xué)習(xí)必備資料
標(biāo)簽： Verilog standard IEEE 1364
上傳時(shí)間： 2013-12-25
上傳用戶：lvzhr
LabVIEW應(yīng)用指令集SCPI（Standard Commands for Programmable Device）
標(biāo)簽： Programmable Commands Standard LabVIEW
上傳時(shí)間： 2013-12-22
上傳用戶：hgy9473
RBFMIP is a package for training multi-instance RBF neural networks
標(biāo)簽： multi-instance networks training package
上傳時(shí)間： 2014-01-23
上傳用戶：refent
BPMLL is a package for training multi-label BP neural networks. The package includes the MATLAB code of the algorithm BP-MLL, which is designed to deal with multi-label learning. It is in particular useful when a real-world object is associated with multiple labels simultaneously
標(biāo)簽： package multi-label includes networks
上傳時(shí)間： 2013-12-05
上傳用戶：xsnjzljj
CCE is a multi-instance learning method solving multi-instance problems through adapting multi-instance representation to single-instance algorithms, which is quite different from existing multi-instance learning algorithms which attempt to adapt single-instance algorithms to multi-instance representation
標(biāo)簽： multi-instance multi-insta adapting learning
上傳時(shí)間： 2014-01-14
上傳用戶：manlian
This toolbox contains re-implementations of four different multi-instance learners, i.e. Diverse Density, Citation-kNN, Iterated-discrim APR, and EM-DD. Ensembles of these single multi-instance learners can be built with this toolbox
標(biāo)簽： i.e. re-implementations multi-instance different
上傳時(shí)間： 2013-12-19
上傳用戶：haohaoxuexi
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