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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Deasik Hong and O. K. Ersoy, "Classification of very high Dimensional data using Neural Networks", IEEE IGARSS,May 1990
[Other Conf. Papers]
조회 78779
Status : | Presented |
---|---|
Date : | 1990-05 |
Title : | Classification of very high Dimensional data using Neural Networks |
Authors : | Deasik Hong and O. K. Ersoy |
Conference : | International Geoscience and Remote Sensing Symposium (IGARSS) |
Abstract : | Neural network models and statistical methods are applied in claasification of very high dimensional data. Both 2-layer and 3-layer iterative neural networks are used in experiments together with a parallel hierarchical neural network. The statistical methods applied include the maximum likelihood method, the minimum Euclidean distance and two "pooling" methods (statistical multisource classificatioin and the linear opinion pool). The data used in experiments are simulated HIRIS data. All the methods are compared based on classification performance with different numbers of features, different numbers of training samples, speed (cpu time) and classification accuracy for training and test data |
URL : | http://ieeexplore.ieee.org/xpl/articleDe...ber=688728 |
Benediktsson, J.A.; Swain, P.H.; Ersoy, O.K.; Hong, D.; , "Classification Of Very High Dimensional Data Using Neural Networks," Geoscience and Remote Sensing Symposium, 1990. IGARSS '90. 'Remote Sensing Science for the Nineties'., 10th Annual International , vol., no., pp.1269-1272, 20-24 May 1990
doi: 10.1109/IGARSS.1990.688728URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=688728&isnumber=3531
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