OFDM
Relay
Cognitive Radio
Multiple Antennas
Resource Allocation
Full Duplex
Spectrum Sensing
Synchronization
Spectrum Sharing
Interference Cancellation
Channel Estimation
Feedback
Heterogeneous Networks
Energy Harvesting
Stochastic Geometry
Bi-directional
Equalization
relay networks
FBMC
HetNet
CDMA
channel capacity
SC-FDMA
interference
MIMO
Ultra Low Power
Duplex
TVWS
CLI
in-band full-duplex system
D-TDD
interference suppression
Reliability
5G
C-V2V
indoor positioning
reinforcement learning
RSRP weighting
User grouping
Rat-dependent positioning
Communication range
multi-access edge computing
hybrid
- Computation offloading
—Device-to-device (D2D)
estimated position updating
control overhead
resource block management
estimated position overlapping
Femtocell
NR positioning
frame structure
Zigbee
body area networks
channel estimation error
Handoff
CoMP
packet delay
power uncertainty
ultra-dense small cell network
GFDM
antenna arrays
5G mobile communication
UFMC
WVAN
mode selection
inter user interference
health care
Vehicular communication
cross-link interference
resource selection
FS-NOMA
Dynamic TDD
Number of training blocks
Mode 3
Uplink SCMA system
Multi-user Receiver
LTE-TDD
user fairness
smart factory
V2X
Metaheuristics
Location-based
QR Factorization
partial overlap
P-NOMA
dynamic HetNet
spectrum partitioning
Cell-free
and 5G networks.
overloading
massive connectivity
DQN
mMIMO
distributed mode
non-orthogonal multiple access
OTDOA
maximum likelihood method
Resource sharing
Power allocation
non-orthogonal multiple access (NOMA)
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Cheolhee Lee and Daesik Hong, "Feature Extraction Using the Bhattacharyya Distance", ICSMC, Oct. 1997
[Other Conf. Papers]
조회 86517
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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