OFDM
Relay
Cognitive Radio
Multiple Antennas
Resource Allocation
Full Duplex
Spectrum Sensing
Synchronization
Spectrum Sharing
Interference Cancellation
Channel Estimation
Feedback
Heterogeneous Networks
Bi-directional
Energy Harvesting
Stochastic Geometry
HetNet
relay networks
FBMC
Equalization
channel capacity
TVWS
CDMA
interference
in-band full-duplex system
Duplex
MIMO
Ultra Low Power
C-V2V
5G
Reliability
SC-FDMA
interference suppression
D-TDD
CLI
indoor positioning
reinforcement learning
RSRP weighting
- Computation offloading
smart factory
Cell-free
multi-access edge computing
estimated position overlapping
—Device-to-device (D2D)
estimated position updating
mMIMO
control overhead
hybrid
NR positioning
Femtocell
Rat-dependent positioning
frame structure
Zigbee
body area networks
channel estimation error
Handoff
CoMP
User grouping
power uncertainty
ultra-dense small cell network
mode selection
antenna arrays
5G mobile communication
UFMC
resource block management
inter user interference
WVAN
health care
partial overlap
GFDM
Dynamic TDD
Multi-user Receiver
Number of training blocks
Uplink SCMA system
V2X
Vehicular communication
cross-link interference
LTE-TDD
FS-NOMA
Location-based
user fairness
Mode 3
QR Factorization
Metaheuristics
P-NOMA
non-orthogonal multiple access
dynamic HetNet
spectrum partitioning
and 5G networks.
massive connectivity
non-orthogonal multiple access (NOMA)
overloading
DQN
OTDOA
distributed mode
Communication range
resource selection
maximum likelihood method
Resource sharing
Power allocation
packet delay
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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 81762
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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