multi uav(多無(wú)人機(jī)matlab下的仿真)
標(biāo)簽： matlab multi uav 無(wú)人機(jī)
上傳時(shí)間： 2014-01-09
上傳用戶：lht618
MSP430單片機(jī)的實(shí)時(shí)多任務(wù)操作系統(tǒng)c源代碼,MSP430 mcu Real Time multi-task operation system source
標(biāo)簽： multi-task MSP 430 operation
上傳時(shí)間： 2014-01-25
上傳用戶：songrui
This GTK+ version 2.12.3. GTK+ is a multi-platform toolkit for creating graphical user interfaces. Offering a complete set of widgets, GTK+ is suitable for projects ranging from small one-off projects to complete application suites.
標(biāo)簽： multi-platform interfaces GTK graphical
上傳時(shí)間： 2016-10-13
上傳用戶：ve3344
Multi-label classification 和weka集成
標(biāo)簽： classification Multi-label weka 集成
上傳時(shí)間： 2013-12-13
上傳用戶：bibirnovis
Discription: This multi-master driver provides the software interface to the I2C Bus hardware of the M3062x series of MitsubishiMCU.
標(biāo)簽： multi-master Discription interface the
上傳時(shí)間： 2016-10-28
上傳用戶：ljt101007
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
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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