OFDM
Relay
Cognitive Radio
Multiple Antennas
Resource Allocation
Full Duplex
Spectrum Sensing
Synchronization
Spectrum Sharing
Channel Estimation
Interference Cancellation
Stochastic Geometry
Energy Harvesting
Feedback
Bi-directional
Heterogeneous Networks
Equalization
HetNet
relay networks
FBMC
Ultra Low Power
SC-FDMA
TVWS
Duplex
Reliability
CDMA
MIMO
interference
channel capacity
in-band full-duplex system
interference suppression
5G
C-V2V
reinforcement learning
RSRP weighting
non-orthogonal multiple access (NOMA)
health care
5G mobile communication
indoor positioning
Vehicle-to-vehicle communication
estimated position overlapping
Resource sharing
Power allocation
multi-access edge computing
control overhead
hybrid
Rat-dependent positioning
NR positioning
smart factory
UFMC
Handoff
Femtocell
QAM
CoMP
power uncertainty
- Computation offloading
amplify and forward communication
Zigbee
body area networks
resource block management
frame structure
WVAN
inter user interference
GFDM
mode selection
antenna arrays
partial overlap
LTE-based V2V
resource selection
maximum likelihood method
Communication range
Number of training blocks
Vehicular communication
Uplink SCMA system
Dynamic TDD
QR Factorization
Metaheuristics
FS-NOMA
cross-link interference
user fairness
Multi-user Receiver
Mode 3
V2X
P-NOMA
dynamic HetNet
spectrum partitioning
DQN
D-TDD
CLI
massive connectivity
and 5G networks.
OTDOA
estimated position updating
distributed mode
non-orthogonal multiple access
Spatial capacity
LTE-TDD
—Device-to-device (D2D)
Location-based
overloading
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Cheolhee Lee and Daesik Hong, "Feature Extraction Using the Bhattacharyya Distance", ICSMC, Oct. 1997
[Other Conf. Papers]
조회 80836
Status : | Presented |
---|---|
Date : | 1997-10 |
Title : | Feature Extraction Using the Bhattacharyya Distance |
Authors : | Cheolhee Lee and Daesik Hong |
Conference : | IEEE International Conference on Systems, Man, and Cybernetics |
Abstract : | The Bhattacharyya distance provides valuable information in determining the effectiveness of a feature set and has been used as a separability measure for feature selection. In Lee (1997), it has been shown that it is feasible to predict the classification error accurately using the Bhattacharyya distance. The new formula makes it possible to estimate classification error between two classes within 1-2% margin. In this paper, we propose a new feature extraction method utilizing the result. Initially, we start with an arbitrary feature vector. Assuming that the feature vector is used for classification, we estimate the classification error using the error estimation formula. Then we move the feature vector slightly in the direction so that the estimated classification error is decreased most rapidly. This can be done by taking a gradient. Experiments show that the proposed method compares favorably with the conventional methods |
URL : | http://ieeexplore.ieee.org/xpl/articleDe...ber=635183 |
Lee, C.; Hong, D.; , "Feature extraction using the Bhattacharyya distance," Systems, Man, and Cybernetics, 1997. Computational Cybernetics and Simulation., 1997 IEEE International Conference on , vol.3, no., pp.2147-2150 vol.3, 12-15 Oct 1997
doi: 10.1109/ICSMC.1997.635183
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=635183&isnumber=13760
doi: 10.1109/ICSMC.1997.635183
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=635183&isnumber=13760
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