模式識別學習綜述.該論文的英文參考文獻為303篇.很有可讀價值.Abstract— Classical and recent results in statistical pattern
recognition and learning theory are reviewed in a two-class
pattern classification setting. This basic model best illustrates
intuition and analysis techniques while still containing the essential
features and serving as a prototype for many applications.
Topics discussed include nearest Neighbor, kernel, and histogram
methods, Vapnik–Chervonenkis theory, and neural networks. The
presentation and the large (thogh nonexhaustive) list of references
is geared to provide a useful overview of this field for both
specialists and nonspecialists.
* acousticfeatures.m: Matlab script to generate training and testing files from event timeseries.
* afm_mlpatterngen.m: Matlab script to extract feature information from acoustic event timeseries.
* extractevents.m: Matlab script to extract event timeseries using the complete run timeseries and the ground truth/label information.
* extractfeatures.m: Matlab script to extract feature information from all acoustic and seismic event timeseries for a given run and set of nodes.
* sfm_mlpatterngen.m: Matlab script to extract feature information from esmic event timeseries.
* ml_train1.m: Matlab script implementation of the Maximum Likelihood Training Module.
?ml_test1.m: Matlab script implementation of the Maximum Likelihood Testing Module.
?knn.m: Matlab script implementation of the k-Nearest Neighbor Classifier Module.
DSR-UU is a DSR implementation that runs in Linux and in the ns-2 network simulator.
DSR-UU implements most of the basic DSR features specified in the DSR
draft (version 10). One big exception is flow extensions.
DSR-UU does NOT use ARP, so do not be surprised if you do not see ARP
traffic. DSR-UU instead uses its own Neighbor table that sets up the
MAC-to-IP translation during route discovery.
In this computer arriving and infecting through MIRC disseminates and duplicates worm s noumenonn of worm file, in mirc catalogue , will produce a script.ini file , will let the worm be disseminated through Mirc communication software by this. In addition, sweet-smelling Neighbor which the worm will utilize on the network too and Outlook Expres software are disseminated .
This project concentrates on the deterministic approach. This guarantees the full coverage in mobile adhoc network. This uses deterministic broadcast protocols that use Neighbor set information only, which is more efficient method.
This project concentrates on the deterministic approach. This guarantees the full coverage in mobile adhoc network. This uses deterministic broadcast protocols that use Neighbor set information only, which is more efficient method.
This project concentrates on the deterministic approach. This guarantees the full coverage in mobile adhoc network. This uses deterministic broadcast protocols that use Neighbor set information only, which is more efficient method.
This project concentrates on the deterministic approach. This guarantees the full coverage in mobile adhoc network. This uses deterministic broadcast protocols that use Neighbor set information only, which is more efficient method.
This project concentrates on the deterministic approach. This guarantees the full coverage in mobile adhoc network. This uses deterministic broadcast protocols that use Neighbor set information only, which is more efficient method.
This project concentrates on the deterministic approach. This guarantees the full coverage in mobile adhoc network. This uses deterministic broadcast protocols that use Neighbor set information only, which is more efficient method.
