International Joint Conference Neural Networks, IJCNN 1999, Washington, DC, USA, July 10-16, 1999. IEEE, 1999. [doi]

Conference: ijcnn1999

Abstract is missing.

- A partial model of cortical memory based on disinhibition1-5 [doi]
- A computational model of synaptic metaplasticity6-11 [doi]
- Generating smooth context-dependent neural representations12-15 [doi]
- Biophysical basis of neural memory16-20 [doi]
- Cerebellar learning: a possible phase switch in evolution21-26 [doi]
- A hybrid model for rodent spatial learning and localization27-32 [doi]
- A numerical exploration of a stochastic model of human list learning33-37 [doi]
- Learning to generate temporal sequences by models of frontal lobes38-41 [doi]
- Oscillatory model of the hippocampal memory42-45 [doi]
- A mathematical analysis of adaptive synapses46-51 [doi]
- A possible mechanism for intermittent oscillations in the KIII model of dynamic memories - the case study of olfaction52-57 [doi]
- Computational model of the entorhinal-hippocampal region derived from a single principle58-63 [doi]
- Generative network explains category formation in Alzheimer patients64-68 [doi]
- Probing the architecture of the brain in experimentation with afterimages69-73 [doi]
- Brain's internal mechanisms - a new paradigm74-79 [doi]
- A connectionist model of motivation80-85 [doi]
- Steps toward development of an integrated neurobiological model of cocaine misuse effects86-90 [doi]
- Neural networks for consciousness: the central representation91-96 [doi]
- Rate of synchrony in locally coupled chains of relaxation oscillators97-102 [doi]
- A neural network model of personality103-108 [doi]
- Modeling higher level processing functions inherent to the human brain109-112 [doi]
- Coordination dynamics in large-scale cortical networks113-116 [doi]
- Artificial learning117-120 [doi]
- A model of lateralization and asymmetries in cortical maps121-124 [doi]
- Pattern completion through thalamo-cortical interaction125-130 [doi]
- Modeling nonsynaptic communication between neurons in the lamina ganglionaris of Musca domestica131-136 [doi]
- A model of saccadic generation based on the neurobiology of the superior colliculus137-141 [doi]
- Design of a low-power, portable sensor system using embedded neural networks and hardware preprocessing142-145 [doi]
- Decoding of information from distributed motor maps146-151 [doi]
- Peculiarities of frequency-phase filtering of signals at different stages of information processing in rat barrel cortex152-155 [doi]
- Local nonuniformity of the visual perception in the peripheral vision field156-159 [doi]
- Analysis of spatial nonlinear responses in cortical complex cells160-163 [doi]
- Knowledge matching model with dynamic weights based on the primary visual cortex164-169 [doi]
- Computational explanations for color transparency170-173 [doi]
- Single-pigment optical mechanism for color opponency in a photoreceptor cell174-177 [doi]
- Cultures of neurons on micro-electrode array and control of their axon growth in hybrid retinal implant178-181 [doi]
- Relating information capacity to a biophysical model for blowfly retina182-187 [doi]
- Diffusive Hebbian model for orientation map formation188-191 [doi]
- Psychophysical test of a tunable retina encoder for retina implants192-195 [doi]
- Realization of geometric illusions using artificial visual model based on acute-angled expansion among crossing lines196-199 [doi]
- Solving the binding problem with feature integration theory200-205 [doi]
- The hippocampus and the brain: a neural network model206-209 [doi]
- Simulated callosal lesions in a neural model of left and right hemispheric regions210-214 [doi]
- Lesion effects in a bihemispheric letter-identification model215-218 [doi]
- Climbing fibre Purkinje cell twins are found219-222 [doi]
- Acquired sensorimotor coordinated signal transformation in a bi-directional neural network model223-228 [doi]
- The inverse identification of neuromuscular system by using neural networks in elbow function self-correcting system229-232 [doi]
- Chaotic phenomena of an active axon233-238 [doi]
- Comparison of fractal characteristics of the electroencephalogram at schoolchildren 10-12 years old in norm and with difficulties in learning239-242 [doi]
- Dependence of one of the fractal characteristics (Hurst exponent) of the human electroencephalogram on the cortical area and type of activity243-246 [doi]
- Spatial knowledge transfer between models of hippocampus and associative cortex247-251 [doi]
- Modeling prefrontal functions for robot navigation252-257 [doi]
- The animal tests of chaotic signal therapy for epilepsy (CSTE)258-261 [doi]
- Control chaos in nonautonomous cellular neural networks using impulsive control methods262-267 [doi]
- Synchronization of the neural response to noisy periodic synaptic input268-273 [doi]
- The separation of speech from interfering sounds: an oscillatory correlation approach274-279 [doi]
- Integrating spatial and temporal mechanisms in auditory neural fiber's computational model280-283 [doi]
- Auditory model based speech feature extraction and its application to speaker identification284-287 [doi]
- A self-organizing two-stream model of language comprehension288-292 [doi]
- Predicting human cortical connectivity for language areas using the Conel database293-295 [doi]
- Synaptic depression may explain many of the temporal response properties observed in primary auditory cortex: a computational investigation296-300 [doi]
- A recurrent network model for range processing of the mustached bat301-304 [doi]
- Analysis of neural response for excitation-inhibition balanced networks with reversal potentials for large numbers of inputs305-308 [doi]
- Overview of electronic nose algorithms309-312 [doi]
- Olfactory signal classification based on evolutionary computation313-316 [doi]
- Odor detection using pulse coupled neural networks317-321 [doi]
- Signal separation processor based on second-order statistic algorithms322-327 [doi]
- Adaptive higher-order feedforward neural networks328-332 [doi]
- Neuron-adaptive higher order neural network group models333-336 [doi]
- "Optimal" neural representation of higher order for quadratic combinatorial optimization337-340 [doi]
- Dynamics of large random recurrent neural networks: oscillations of 2-population model341-344 [doi]
- Modeling a compression plant using recurrent neural networks345-348 [doi]
- Multi-step-ahead prediction using dynamic recurrent neural networks349-352 [doi]
- A simplex optimization approach for recurrent neural network training and for learning time-dependent trajectory patterns353-358 [doi]
- Training recurrent neural networks with noisy input measurements359-363 [doi]
- Mathematical justification of recurrent neural networks with long and short-term memories364-369 [doi]
- Approximation to continuous functionals and operators using adaptive higher-order feedforward neural networks370-374 [doi]
- Estimation with two hidden layer neural nets375-378 [doi]
- Synthesis of multilayer neural networks architecture (for the case of cascaded NNs)379-382 [doi]
- Application of SVM to Lyapunov function approximation383-387 [doi]
- Piecewise linear networks (PLN) for function approximation388-391 [doi]
- Prediction of nonlinear dynamical system output with multilayer perceptron and radial basis function neural networks392-397 [doi]
- Time-delay polynomial networks and quality of approximation398-403 [doi]
- Hamiltonian vector field for the Lorenz invariant set404-408 [doi]
- Learning functions and their derivatives using Taylor series and neural networks409-412 [doi]
- A new EM algorithm using Tikhonov regularization413-418 [doi]
- Approximation of a function and its derivatives in feedforward neural networks419-421 [doi]
- Increase the number of stable equilibrium points in a Hopfield-type neural network422-426 [doi]
- New approach to the storage capacity of neural networks using the minimum distance between input patterns427-431 [doi]
- Extending the power and capacity of constraint satisfaction networks432-437 [doi]
- Evolving an optimal de/convolution function for the neural net modules of ATR's artificial brain project438-443 [doi]
- Pattern coding based on firing times in a network of spiking neurons444-449 [doi]
- The random subspace coarse coding scheme for real-valued vectors450-455 [doi]
- Enhanced artificial neural networks using complex numbers456-461 [doi]
- Implementing competitive learning in a quantum system462-466 [doi]
- On model selection in SLT and linear basis neural networks467-472 [doi]
- The generalized eigen-decomposition approach to blind source problems473-476 [doi]
- A learning-theory-based training algorithm for variable-structure dynamic neural modeling477-482 [doi]
- Symbolized particles store type neuron model and its application483-487 [doi]
- Fourier neural networks488-491 [doi]
- Colimits in memory: category theory and neural systems492-496 [doi]
- A boundary-pair representation for perception modeling497-501 [doi]
- On logical semantics of hybrid symbolic-neural networks for commonsense reasoning502-505 [doi]
- Exploration of mean-field approximation for feedforward networks506-509 [doi]
- 2D order of self-organizing Kristal maps510-513 [doi]
- Dual coding in a network of spiking neurons: aperiodic spikes and stable firing rates514-518 [doi]
- Some new results on the capabilities of integer weights neural networks in classification problems519-524 [doi]
- Oscillator neural network model with distributed native frequencies525-528 [doi]
- An instantaneous topological mapping model for correlated stimuli529-534 [doi]
- Parameter selection and state dominance in hidden Markov models of neuronal activity535-539 [doi]
- Bayesian ying-yang supervised learning, modular models, and three layer nets540-545 [doi]
- BYY data smoothing based learning on a small size of samples546-551 [doi]
- Bayesian ying-yang theory for empirical learning, regularization and model selection: general formulation552-557 [doi]
- Maximum/minimum detection by a module-based neural network with redundant architecture558-561 [doi]
- A method for investigating the nonlinear dynamics of the human brain from analysis of functional MRI data562-567 [doi]
- Theoretical analysis of quantized Hopfield network for integer programming568-571 [doi]
- Validation of fusion through linear programming572-575 [doi]
- Nonlinear factorization in the hippocampal neural structure576-581 [doi]
- Primal neural networks for solving convex quadratic programs582-587 [doi]
- A dual neural network solving quadratic programming problems588-593 [doi]
- Nonlinear programming with feedforward neural networks594-598 [doi]
- Internal representation in networks of nonmonotonic processing units599-604 [doi]
- Morphological perceptrons605-610 [doi]
- Uniform approximation of discrete-time nonlinear systems611-616 [doi]
- New single neuron structure for solving nonlinear problems617-620 [doi]
- Neighborhood induced stochastic resonance621-624 [doi]
- Factors controlling generalization ability of MLP networks625-630 [doi]
- Neural networks in non-Euclidean metric spaces631-636 [doi]
- Using neural networks as an aid for solving engineering problems637-642 [doi]
- A zero order minimization algorithm using a neural network643-645 [doi]
- An extended Hopfield model for combinatorial optimization646-649 [doi]
- The robustness of relaxation rates in constraint satisfaction networks650-654 [doi]
- Neural networks in 2-D continuous time655-658 [doi]
- What can memorization learning do?659-662 [doi]
- The optimal value of self-connection663-668 [doi]
- Optimization of logical rules derived by neural procedures669-674 [doi]
- Solving a graph layout problem using strictly digital neural networks with virtual slack-neurons675-679 [doi]
- Hysteresis neural networks for a combinatorial optimization problem680-683 [doi]
- Design and analysis of neural networks for systems optimization684-689 [doi]
- Locally coupled chaotic oscillator network for scene segmentation690-694 [doi]
- Synchronized chaos in coupled neuromodules of different types695-698 [doi]
- Self-trapping in an attractor neural network with nearest neighbor synapses mimics full connectivity699-704 [doi]
- Relationship between fault tolerance, generalization and the Vapnik-Chervonenkis (VC) dimension of feedforward ANNs705-709 [doi]
- Noise reduction of chaotic interspike intervals (ISI) of neurons710-712 [doi]
- On the self-feedback controlled chaotic neural network and its application to N-Queen problem713-716 [doi]
- Integrate-and-fire model with periodic inputs717-720 [doi]
- Autonomous integrate-and-fire model with vibration721-724 [doi]
- A novel dynamical invariant measure addresses the stability of the chaotic KIII neural network725-729 [doi]
- Chaos in neural networks with bistable dendrite membrane current-voltage relation730-735 [doi]
- Stochastic chaos versus deterministic chaos: a case for analog versus digital embodiment of devices for pattern recognition736-741 [doi]
- A self-organizing network system forming memory from nonstationary probability distributions742-745 [doi]
- Knowledge processing system using chaotic associative memory746-751 [doi]
- Chaotic associative memory for sequential patterns752-757 [doi]
- A new bidirectional associative memory based on distributed representation758-763 [doi]
- A combined multi-Winner multidirectional associative memory764-769 [doi]
- Comparison between theory and simulation for the two-level decoupled Hamming associative memory770-774 [doi]
- Enhancing the storage capacity of binary associative memories775-780 [doi]
- Capacity of second-ordered bidirectional associative memory in the presence of noise bits781-786 [doi]
- Determining field of visual attention in associative memories787-790 [doi]
- An architecture for an adaptive associative memory system based on autonomous reactions between images791-794 [doi]
- Behavior of interactive neural networks as associative memories795-797 [doi]
- A learning method for synthesizing associative memory in neural networks798-803 [doi]
- Partitioned architectures for large scale data recovery804-807 [doi]
- Priority ordered architecture of neural networks808-811 [doi]
- Boosting the performance of weightless neural networks by using a postprocessing transformation of the output scores812-816 [doi]
- Approximation of chaotic shapes with tree-structured neural networks817-820 [doi]
- Genetic optimization of NN topologies for the task of natural language processing821-826 [doi]
- Nonlinear channel equalization using new neural network model827-830 [doi]
- Algorithmically universal model of structureless parallelism831-835 [doi]
- Performance improvement of the BSB model in classifier tasks using optimization techniques836-840 [doi]
- On the conditions of outer-supervised feedforward neural networks for null cost learning841-845 [doi]
- An adjustable model for linear to nonlinear regression846-850 [doi]
- The modular map851-856 [doi]
- Using factorial design to optimise neural networks857-861 [doi]
- Perceptrons revisited: the addition of a non-monotone recursion greatly enhances their representation and classification properties862-867 [doi]
- Towards a high performance neural branch predictor868-873 [doi]
- A spatial quantum neural computer874-877 [doi]
- Inverse PCA method for weight initialization in CMLP network878-881 [doi]
- Locally linear independent component analysis882-887 [doi]
- A comparison of neural ICA algorithms using real-world data888-893 [doi]
- A fast algorithm for estimating overcomplete ICA bases for image windows894-899 [doi]
- Multiclass least squares support vector machines900-903 [doi]
- On support vector decision trees for database marketing904-909 [doi]
- Loss function for blind source separation-minimum entropy criterion and its generalized anti-Hebbian rules910-915 [doi]
- Optimal dimension reduction and transform coding with mixture principal components916-920 [doi]
- An information theoretic method for designing multiresolution principal component transforms921-926 [doi]
- MICA: multimodal independent component analysis927-932 [doi]
- Learning algorithm for independent component analysis by geodesic flows on orthogonal group933-938 [doi]
- A VLSI friendly algorithm for support vector machines939-942 [doi]
- Moderating the outputs of support vector machine classifiers943-948 [doi]
- Temporal BYY learning and its applications to extended Kalman filtering, hidden Markov model, and sensor-motor integration949-954 [doi]
- An ICA algorithm with adaptive-learned polynomial nonlinearity for signal separation955-960 [doi]
- Autoregressive signal separation approach with seesaw-mapping technique on temporal source separation961-964 [doi]
- On the study of BKYY cluster number selection criterion for small sample data set with bootstrap technique965-968 [doi]
- Support vector approaches for engine knock detection969-974 [doi]
- Hybrid neural networks for frequency estimation of unevenly sampled data975-979 [doi]
- Adaptive blind MIMO system identification using principal component neural models980-984 [doi]
- 'Mechanical' neural learning and InfoMax orthonormal independent component analysis985-988 [doi]
- Input variable selection using independent component analysis989-992 [doi]
- Real world blind separation of convolved speech signals993-997 [doi]
- Accelerated training of support vector machines998-1003 [doi]
- Nonlinear methods for clustering and reduction of dimensionality1004-1009 [doi]
- Local-to-global topological methods in data analysis and applications to fMRI of human brain1010-1015 [doi]
- A local face statistics recognition methodology beyond ICA and/or PCA1016-1021 [doi]
- Early vision image analyses using ICA in unsupervised learning ANN1022-1027 [doi]
- Implementing neural networks into modern technology1028-1032 [doi]
- Blind de-mixing real-time algorithm of piecewise time series mixture1033-1037 [doi]
- Maximum entropy ICA constrained by individual entropy maximization employing self-organizing maps1038-1042 [doi]
- Adaptive algorithms for accelerated PCA from an augmented Lagrangian cost function1043-1048 [doi]
- Thermodynamics proof of sensory learning and implication of mammal homeostasis1049-1053 [doi]
- Blind separation of convolutive mixtures1054-1058 [doi]
- Independent subspace analysis shows emergence of phase and shift invariant features from natural images1059-1064 [doi]
- Multi-resolution support vector machine1065-1070 [doi]
- Temporal Bayesian Ying-Yang dependence reduction, blind source separation and principal independent components1071-1076 [doi]
- Performance of RBF equalizer in data storage channels1077-1080 [doi]
- A game-theoretic formulation on adaptive categorization in ART networks1081-1086 [doi]
- Self-organization of shift-invariant receptive fields1087-1091 [doi]
- A self-scaling procedure in unsupervised correlational neural networks1092-1096 [doi]
- Unsupervised curve-based clustering1097-1101 [doi]
- Unsupervised context-based learning of multiple temporal sequences1102-1106 [doi]
- Probabilistic principal surfaces1107-1112 [doi]
- A study of parallel neural networks1113-1116 [doi]
- Analytical results on pseudo-polynomial functional-link neural units for blind density shaping1117-1120 [doi]
- Feature extraction from data structures with unsupervised recursive neural networks1121-1126 [doi]
- Parallel self-organization map using multiple stimuli1127-1130 [doi]
- Self-creating and adaptive learning of RBF networks: merging soft-competition clustering algorithm with network growth technique1131-1135 [doi]
- A cluster analysis based on a regularization method1136-1139 [doi]
- A density based membership function for fuzzy clustering1140-1143 [doi]
- A novel neural network for four-term analogy based on area representation1144-1149 [doi]
- Multi-level neural networks1150-1153 [doi]
- Analyzing weight distribution of neural networks1154-1157 [doi]
- The two spirals benchmark: lessons from the hidden layers1158-1163 [doi]
- A new framework for modeling learning dynamics1164-1168 [doi]
- Inductive sorting-out GMDH algorithms with polynomial complexity for active neurons of neural network1169-1173 [doi]
- When local isn't enough: extracting distributed rules from networks1174-1179 [doi]
- Validation of neural networks using hybrid resampling methods1180-1184 [doi]
- Looking inside the ANN "black box": classifying individual neurons as outlier detectors1185-1188 [doi]
- Neural networks input selection by using the training set1189-1194 [doi]
- Dimensionality reduction using a novel neural network based feature extraction method1195-1198 [doi]
- Visualization of radial basis function networks1199-1202 [doi]
- Gauss-sigmoid neural network1203-1208 [doi]
- Convergence analysis of the Quickprop method1209-1214 [doi]
- Confidence and prediction intervals for neural network ensembles1215-1218 [doi]
- Discretization methods for encoding of continuous input variables for Boolean neural networks1219-1224 [doi]
- Parallel, self organizing, consensus neural networks1225-1228 [doi]
- A neural network for learning domain rules with precision1229-1233 [doi]
- Learning in a quantizable neural network1234-1238 [doi]
- Powell's dogleg trust-region steps with the quasi-Newton augmented Hessian for neural nonlinear least-squares learning1239-1244 [doi]
- Sample path-based policy-only learning by actor neural networks1245-1250 [doi]
- Direction-basis-function neural networks1251-1254 [doi]
- Z splitting criterion for growing trees and boosting1255-1258 [doi]
- The learning behavior of single neuron classifiers on linearly separable or nonseparable input1259-1264 [doi]
- RBF neural networks with centers assignment via Karhunen-Loeve transform1265-1270 [doi]
- A comparison of Eclectic learning and Stagger1271-1274 [doi]
- Iterative Improvement of trigonometric networks1275-1280 [doi]
- Optimal use of regularization and cross-validation in neural network modeling1281-1286 [doi]
- Ensemble learning using observational learning theory1287-1292 [doi]
- A comparison between two interval arithmetic learning algorithms1293-1297 [doi]
- Object oriented learning network and its applications1298-1301 [doi]
- Universal learning networks with varying parameters1302-1307 [doi]
- Size-reducing RBF networks1308-1312 [doi]
- Improved mutual information feature selector for neural networks in supervised learning1313-1318 [doi]
- Incremental class learning-an approach to longlife and scalable learning1319-1324 [doi]
- Rival rewarded and randomly rewarded rival competitive learning1325-1328 [doi]
- Biofunctionality: a novel learning method for intelligent agents1329-1332 [doi]
- Efficient training techniques for classification with vast input space1333-1338 [doi]
- Convergent design of a piecewise linear neural network1339-1344 [doi]
- Improved generalisation using cooperative learning and rule extraction1345-1349 [doi]
- Incremental learning using sensitivity analysis1350-1355 [doi]
- Hebbian learning and competition in the neural abstraction pyramid1356-1361 [doi]
- Iterative fast orthogonal search algorithm for sparse self-structuring generalized single-layer networks1362-1367 [doi]
- The α-EM learning and its cookbook: from mixture-of-expert neural networks to movie random field1368-1373 [doi]
- Non-iterative learning for neural networks1374-1379 [doi]
- A novel fast learning algorithms for time-delay neural networks1380-1383 [doi]
- Preconditioning method to accelerate neural networks gradient training algorithms1384-1388 [doi]
- A new learning algorithm without explicit error backpropagation1389-1392 [doi]
- Weighted least square ensemble networks1393-1396 [doi]
- Mediated and multi-level information processing1397-1402 [doi]
- Controlling simple structural information to improve generalization performance1403-1408 [doi]
- Combining cross-validation and confidence to measure fitness1409-1414 [doi]
- Adaptive training methods for optimal margin classification1415-1418 [doi]
- Approximate maximum entropy joint feature inference for discrete space classification1419-1424 [doi]
- Lotto-type competitive learning and its stability1425-1428 [doi]
- Neural networks to estimate ML multi-class constrained conditional probability density functions1429-1432 [doi]
- A general formulation for learning multi-class posterior probabilities1433-1437 [doi]
- Selection of training samples for learning with hints1438-1441 [doi]
- Examination of effectiveness of higher-order mean field Boltzmann machine learning based on linear response theorem1442-1445 [doi]
- Efficient kernel functions for the general regression and modified probabilistic neural networks1446-1449 [doi]
- A dynamic mapping based on probabilistic relaxation1450-1455 [doi]
- Averaging ensembles of self-organising mixture networks for density estimation1456-1460 [doi]
- A new method for density estimation by using forward neural network1461-1464 [doi]
- Conditional entropy minimization in neural network classifiers1465-1470 [doi]
- On weight initialization in cascade-correlation learning1471-1474 [doi]
- Recurrent auto-associative networks and sequential processing1475-1480 [doi]
- Continuous time NLq theory: absolute stability criteria1481-1484 [doi]
- Complex bilinear recurrent neural network for equalization of a digital satellite channel1485-1490 [doi]
- Optimization of recursive branching network1491-1495 [doi]
- Recursive orthogonal least squares learning with automatic weight selection for Gaussian neural networks1496-1500 [doi]
- Improved second-order training algorithms for globally and partially recurrent neural networks1501-1506 [doi]
- Cascade neural networks in variational methods for boundary value problems1507-1510 [doi]
- A partially recurrent mixture-of-experts model for task decomposition into temporal and static subtasks1511-1514 [doi]
- Recurrent high-order networks for probabilistic explanation1515-1520 [doi]
- Training recurrent network with block-diagonal approximated Levenberg-Marquardt algorithm1521-1526 [doi]
- Recurrent neural networks can learn simple, approximate regular languages1527-1532 [doi]
- Dynamic targets - adapting supervised learning to time series classification1533-1537 [doi]
- Forecasting chaotic time series using neuro-fuzzy approach1538-1543 [doi]
- A gain perturbation method to improve the generalization performance for the recurrent neural network misfire detector1544-1549 [doi]
- Pattern recognition and learning in bistable CAM networks1550-1553 [doi]
- Incremental adaptation of resource-allocating network for non-stationary time series1554-1559 [doi]
- Estimates of constrained multi-class a posteriori probabilities in time series problems with neural networks1560-1561 [doi]
- Dynamic logistic regression1562-1567 [doi]
- Components for a sequence processing neural network1568-1573 [doi]
- Construction of recurrent mixture models for time series classification1574-1579 [doi]
- Training multi-loop networks1580-1585 [doi]
- A robust learning algorithm for the extended AM neural network1586-1589 [doi]
- A learning algorithm for a hybrid nonlinear predictor applied to noisy nonlinear time series1590-1593 [doi]
- A novel approach for training neural networks for long-term prediction1594-1599 [doi]
- Cascade error projection with low bit weight quantization for high order correlation data1600-1603 [doi]
- On optimal usage of centroid units in CMLP network1604-1607 [doi]
- The little neuron that could1608-1613 [doi]
- Cross validation and MLP architecture selection1614-1619 [doi]
- Adaptive multilayer perceptrons1620-1625 [doi]
- Initializing multilayer perceptrons with interconnected neurons1626-1630 [doi]
- Classification complexity and its estimation algorithm for two-class classification problem1631-1634 [doi]
- Optimal learning rates for each pattern and neuron in gradient descent training of multilayer perceptrons1635-1638 [doi]
- Huber optimization of neural networks: a robust training method [microwave modeling]1639-1642 [doi]
- Enhancing incremental learning in MLP networks using ensemble encoding of network inputs1643-1645 [doi]
- An hybrid architecture for active and incremental learning: the self-organizing perceptron (SOP) network1646-1651 [doi]
- Estimation of initial weights and hidden units for fast learning of multilayer neural networks for pattern classification1652-1656 [doi]
- A multilayer neural network with nonlinear inputs and trainable activation functions: structure and simultaneous learning algorithm1657-1661 [doi]
- Bayesian neural networks with correlating residuals1662-1665 [doi]
- Quadrant-distance graphs: a method for visualizing neural network weight spaces1666-1671 [doi]
- New block recursive MLP training algorithms using the Levenberg-Marquardt algorithm1672-1677 [doi]
- Statistical method of pruning neural networks1678-1680 [doi]
- A learning algorithm for multilayer perceptron as classifier1681-1684 [doi]
- Thermometer coding for multilayer perceptron learning on continuous mapping problems1685-1690 [doi]
- Multilayer perceptron based dimensionality reduction1691-1695 [doi]
- A novel neural learning algorithm for multilayer perceptrons1696-1701 [doi]
- New methods to train a BP network and their application1702-1705 [doi]
- Improved CBP learning with output bias decomposition1706-1709 [doi]
- Adaptability of the backpropagation procedure1710-1715 [doi]
- Training MLPs layer-by-layer with the information potential1716-1720 [doi]
- Multi-gradient: a fast converging and high performance learning algorithm1721-1724 [doi]
- Efficient algorithm for training neural networks with one hidden layer1725-1728 [doi]
- Acceleration of learning speed in neural networks by reducing weight oscillations1729-1732 [doi]
- Curved search algorithm for neural network learning1733-1736 [doi]
- Learning efficiency improvement of back propagation algorithm by error saturation prevention method1737-1742 [doi]
- A new backpropagation learning algorithm1743-1748 [doi]
- ACL-adaptive correction of learning parameters for backpropagation based algorithms1749-1752 [doi]
- Pattern grouping strategy makes BP algorithm less sensitive to learning rate1753-1756 [doi]
- A new adaptive scheme for the backpropagation algorithm1757-1761 [doi]
- Nonmonotone methods for backpropagation training with adaptive learning rate1762-1767 [doi]
- Sign-methods for training with imprecise error function and gradient values1768-1773 [doi]
- Sliding mode backpropagation: control theory applied to neural network learning1774-1778 [doi]
- Delta learning law for a single neuron1779-1782 [doi]
- An introduction to information theoretic learning1783-1787 [doi]
- Avoiding overfitting caused by noise using a uniform training mode1788-1793 [doi]
- Activation functions with learnable amplitude1794-1798 [doi]
- The impact of the error function selection in neural network-based classifiers1799-1804 [doi]
- Sparse algorithm for feed-forward neural networks1805-1808 [doi]
- Roles of learning rates, artificial process noise and square root filtering for extended Kalman filter training1809-1814 [doi]
- A class of learning for optimal generalization1815-1819 [doi]
- Feedforward networks with monotone constraints1820-1823 [doi]
- Extended Kalman filter learning algorithm for hyper-complex multilayer neural networks1824-1828 [doi]
- Variance analysis of sensitivity information for pruning multilayer feedforward neural networks1829-1833 [doi]
- Input selection by multilayer feedforward trained networks1834-1839 [doi]
- Generalization capability of one and two hidden layers1840-1843 [doi]
- Subgoal chaining and the local minimum problem1844-1849 [doi]
- Acceleration of learning in feedforward networks using dynamical systems analysis and matrix perturbation theory1850-1855 [doi]
- Feature selection in codebook based methods provides high accuracy1856-1860 [doi]
- Using multiplicative algorithms to build cascade correlation networks1861-1865 [doi]
- AINS: architecture independent neuron selection1866-1869 [doi]
- Online least-squares training for the underdetermined case1870-1875 [doi]
- Pattern recognition with spiking neurons: performance enhancement based on a statistical analysis1876-1880 [doi]
- Optimal training parameters in multilayer feedforward networks1881-1884 [doi]
- A general CAC approach using novel ant algorithm training based neural network1885-1888 [doi]
- Cascade steepest descent learning algorithm for multilayer feedforward neural network1889-1894 [doi]
- Growing smaller networks with the tiling algorithm1895-1899 [doi]
- Time topology for the self-organizing map1900-1905 [doi]
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