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IJCNN 2005: Montreal, QC, Canada
- IEEE International Joint Conference on Neural Networks, IJCNN 2005, Montreal, QC, Canada, July 31 - August 4, 2005. IEEE 2005, ISBN 0-7803-9048-2
- Donald C. Wunsch:
The INNS President's Welcome. - Pierre Baldi:
Exploring chemical space with computers: challenges and opportunities. 83 - David P. Casasent, Yu-Chiang Wang:
Automatic target recognition using new support vector machine. 84-89 - John A. Drakopoulos, Ahmad Abdulkader:
Training with heterogeneous data. 90-95 - Takahiro Emoto, Masatake Akutagawa, Udantha R. Abeyratne, Hirofumi Nagashino, Yohsuke Kinouchi:
Tracking the states of a nonlinear system in the weight-space of a feed-forward neural network. 96-101 - Jingdong Wang, James T. Kwok, Helen C. Shen, Long Quan:
Data-dependent kernels for high-dimensional data classification. 102-107 - Hui Kong, Xuchun Li, Lei Wang, Earn Khwang Teoh, Jian-Gang Wang, Ronda Venkateswarlu:
Generalized 2D principal component analysis. 108-113 - Sepp Hochreiter, Klaus Obermayer:
Optimal gradient-based learning using importance weights. 114-119 - Walter J. Freeman:
Cinematographic construction by brains of knowledge from information. 120-124 - Robert Kozma, Mark Myers:
Analysis of phase transitions in KIV with amygdala during simulated navigation control. 125-130 - Barani Raman, Ricardo Gutierrez-Osuna:
Mixture segmentation and background suppression in chemosensor arrays with a model of olfactory bulb-cortex interaction. 131-136 - Derek Harter:
Evolving neurodynamic controllers for autonomous robots. 137-142 - Antonino Staiano, Angelo Ciaramella, Giancarlo Raiconi, Roberto Tagliaferri, Roberto Amato, Giuseppe Longo, Gennaro Miele, Ciro Donalek:
Data visualization methodologies for data mining systems in bioinformatics. 143-148 - Alberto Bertoni, Giorgio Valentini:
Random projections for assessing gene expression cluster stability. 149-154 - Francesco Masulli, Stefano Rovetta:
A new approach to hierarchical clustering for the analysis of genomic data. 155-160 - Alberto Paccanaro, Valery Trifonov, Haiyuan Yu, Mark Gerstein:
Inferring protein-protein interactions using interaction network topologies. 161-166 - Kamel Saadi, Kee Khoon Lee, Gavin C. Cawley, Michael W. Bevan:
Predicting sugar regulation in Arabidopsis thaliana using kernel learning methods. 167-172 - Alexandru G. Floares:
Feedback linearization using neural networks applied to advanced pharmacodynamic and pharmacogenomic systems. 173-178 - Vasilis Zorkadis, M. Panayotou, Dimitrios A. Karras:
Improved spam e-mail filtering based on committee machines and information theoretic feature extraction. 179-184 - Markus Harva, Ata Kabán:
A variational Bayesian method for rectified factor analysis. 185-190 - Kyu-Hwa Jeong, Jian-Wu Xu, José C. Príncipe:
An information theoretic approach to adaptive system training using unlabeled data. 191-195 - Xuchun Li, Lei Wang, Eric Sung:
A study of AdaBoost with SVM based weak learners. 196-201 - Ryotaro Kamimura:
Information maximization and cost minimization in information-theoretic competitive learning. 202-207 - Umut Ozertem, Deniz Erdogmus:
Maximally discriminative spectral feature projections using mutual information. 208-213 - Carlos Alzate, Johan A. K. Suykens:
Extending kernel principal component analysis to general underlying loss functions. 214-219 - Washington Mio, Qiang Zhang, Xiuwcn Liu:
Nonlinearity and optimal component analysis. 220-225 - Makoto Yamada, Ali Pezeshki, Mahmood R. Azimi-Sadjadi:
Relation between kernel CCA and kernel FDA. 226-231 - Kyungsuk David Lee, Michelle Jeungeun Lee, Soo-Young Lee:
Extraction of frame-difference features based on PCA and ICA for lip-reading. 232-237 - Masahiro Kimura, Kazumi Saito, Naonori Ueda:
Multinomial PCA for extracting major latent topics from document streams. 238-243 - Thang Viet Nguyen, Jagdish Chandra Patra, Amitabha Das, Geok See Ng:
Post nonlinear blind source separation by geometric linearization. 244-249 - Holger Fröhlich, Björn Naundorf, Maxim Volgushev, Fred Wolf:
Which features trigger action potentials in cortical neurons in vivo? 250-255 - John G. Taylor, Matthew Hartley, Neill R. Taylor:
Attention as sigma-pi controlled ACh-based feedback. 256-261 - Jason Satel, Thomas Trappenberg, Raymond M. Klein:
Motivational modulation of endogenous inputs to the superior colliculus. 262-267 - Heejin Lim, Yoonsuck Choe:
Facilitatory neural activity compensating for neural delays as a potential cause of the flash-lag effect. 268-273 - Peng Xu, Pamela Abshire:
Fisher information quantifies task-specific performance in the blowfly photoreceptor. 274-279 - Stanley Gotshall, Christopher Canine, Benjamin Jennings, Terence Soule:
Evolutionary training of a biologically realistic spino-neuromuscular system. 280-285 - Rui Xu, Donald C. Wunsch II:
Gene regulatory networks inference with recurrent neural network models. 286-291 - Xiaoxu Xiong, Derong Liu, Huaguang Zhang:
A self-organizing neural network approach for the identification of motifs with insertions and deletions in protein sequences. 292-297 - Nora Speer, Holger Fröhlich, Christian Spieth, Andreas Zell:
Functional grouping of genes using spectral clustering and Gene Ontology. 298-303 - Gustavo Bastos, Katia S. Guimarães:
A simpler Bayesian network model for genetic regulatory network inference. 304-309 - Paul C. Conilione, Dianhui Wang:
Effect of non-target examples on E.coli promoters recognition using neural networks. 310-315 - Bing Wang, De-Shuang Huang, Peng Chen, Yunping Zhu, Yixue Li:
Predicting protein-protein interactions based on protein-domain relationships. 316-319 - Alex Kalos:
Automated heuristic growing of neural networks for nonlinear time series models. 320-325 - Wei Jiang, Seong G. Kong, Gregory D. Peterson:
ECG signal classification using block-based neural networks. 326-331 - Ashraf M. Abdelbar, Ossama Soliman, Sherif Kinawy, Hisham Sayed:
An evolved Seega player capable of strong novice-level play. 332-336 - Dong-Wook Lee, Seong G. Kong, Kwee-Bo Sim:
Evolvable neural networks based on developmental models for mobile robot navigation. 337-342 - Mehmet K. Muezzinoglu, Jacek M. Zuracla:
A recurrent RBF network model for nearest neighbor classification. 343-348 - Patricia Melin, Claudia I. González, Felma Gonzalez, Oscar Castillo:
Face recognition using modular neural networks and fuzzy Sugeno integral for response integration. 349-354 - Frederick G. Harmon, Andrew A. Frank, Sanjay S. Joshi:
Application of a CMAC neural network to the control of a parallel hybrid-electric propulsion system for a small unmanned aerial vehicle. 355-360 - Luis J. Ricalde, Edgar N. Sánchez:
Inverse optimal nonlinear recurrent high order neural observer. 361-365 - Joy Mazumdar, Ronald G. Harley, Franklin C. Lambert:
System and method for determining harmonic contributions from non-linear loads using recurrent neural networks. 366-371 - Bernardino Castillo-Toledo, A. Hernández Avalos:
On output regulation for SISO nonlinear systems with dynamic neural networks. 372-377 - Rong-Jong Wai, Chia-Chin Chu:
Indirect field-oriented linear induction motor drive with Petri fuzzy-neural-network control. 378-383 - Tsu-Tian Lee, Chih-Min Lin, Chun-Fei Hsu:
Wavelet-neural-network-based backstepping control for chaotic systems. 384-389 - Anatoli Gorchetchnikov, Massimiliano Versace, Michael E. Hasselmo:
Spatially and temporally local spike-timing-dependent plasticity rule. 390-395 - Dominic I. Standage, Thomas Trappenberg:
Differences in the subthreshold dynamics of leaky integrate-and-fire and Hodgkin-Huxley neuron models. 396-399 - Toshimichi Saito, Yoshio Kon'no, Hiroyuki Torikai:
Rich phenomena of pulse-coupled spiking neurons with triangular waveform input. 400-404 - Katsumi Tateno, Hatsuo Hayashi, Satoru Ishizuka:
Synchronized theta rhythm selection in a dentate gyrus network model. 405-409 - Ashok Patel, Bart Kosko:
Noise benefits in spiking retinal and sensory neuron models. 410-415 - Guoping Wang, Misha Pavel:
A spiking neuron representation of auditory signals. 416-421 - Gavyn Pang, Jagath C. Rajapakse:
Comparative genomic study of Parkinson's disease candidate genes. 422-427 - Rasmus Storjohann, Gary F. Marcus:
NeuroGene: integrated simulation of gene regulation, neural activity and neurodevelopment. 428-433 - Michail Maniadakis, Panos E. Trahanias:
A hierarchical coevolutionary method to support brain-lesion modelling. 434-439 - Germano Resconi:
Genome space and structure genome invariants. 440-445 - Nikola K. Kasabov, Lubica Benuskova, Simei Gomes Wysoski:
A computational neurogenetic model of a spiking neuron. 446-451 - Gail A. Carpenter, Siegfried Martens:
Self-organizing hierarchical knowledge discovery by an ARTMAP information fusion system. 452-456 - Michael J. Healy, Richard D. Olinger, Robert J. Young, Thomas P. Caudell, Kurt W. Larson:
Modification of the ART-1 architecture based on category theoretic design principles. 457-462 - Dinani Gomes, Manuel Fernández Delgado, Senén Barro:
A vigilance-free ART network with general geometry internal categories. 463-468 - Ross Peralta, Georgios C. Anagnostopoulos, Eduardo Gómez-Sánchez, Samuel Richie:
On the design of an ellipsoid ARTMAP classifier within the fuzzy adaptive system ART framework. 469-474 - Jimmy Secretan, José Castro, Michael Georgiopoulos, Joe Tapia, Amit Chadha, Brian Huber, Georgios C. Anagnostopoulos, Samuel Richie:
Parallelizing the fuzzy ARTMAP algorithm on a Beowulf cluster. 475-480 - Yi Sun, Mark Robinson, Rod Adams, Paul Kaye, Alistair G. Rust, Neil Davey:
Using real-valued meta classifiers to integrate binding site predictions. 481-486 - Marcilio C. P. de Souto, Shirly C. M. Silva, Valnaide G. Bittencourt, Daniel S. A. de Araujo:
Cluster ensemble for gene expression microarray data. 487-492 - Jing-Jing Li, De-Shuang Huang, Robert M. MacCallum, Xiao-Run Wu:
Characterizing human gene splice sites using evolved regular expressions. 493-498 - Peng Chen, De-Shuang Huang, Bing Wang, Yunping Zhu, Yixue Li:
Prediction of contact map integrated PNN with conformational energy. 499-502 - Jagdish Chandra Patra, Lei Wang, Ee Luang Ang, Narendra S. Chaudhari:
Neural network-based analysis of DNA microarray data. 503-508 - Jagdish Chandra Patra, Qin Zhen, Ee Luang Ang, Amitabha Das:
Neural networks for gene expression analysis and gene selection from DNA microarray. 509-514 - Mihail Bota, Larry W. Swanson:
The molecules module of the brain architecture management system. 515-520 - Xue-Wen Chen, Jeremy Chen:
Protein flexibility modeling using kernel based methods. 521-526 - Valnaide G. Bittencourt, Márjory C. C. Abreu, Marcilio C. P. de Souto, Anne Magály de Paula Canuto:
An empirical comparison of individual machine learning techniques and ensemble approaches in protein structural class prediction. 527-531 - BaiFang Zhang, Zhihang Chen, Yi Lu Murphey:
Protein secondary structure prediction using machine learning. 532-537 - Jinmiao Chen, Narendra S. Chaudhari:
Protein secondary structure prediction with a hybrid RNN/HMM system. 538-541 - Terje Kristensen, Gaurav Kumar:
Entropy based disease classification of proteomic mass spectrometry data of the human serum by a support vector machine. 542-545 - Man Lan, Sam Yuan Sung, Hwee-Boon Low, Chew Lim Tan:
A comparative study on term weighting schemes for text categorization. 546-551 - Takashi Onoda, Hiroshi Murata, Seiji Yamada:
One class support vector machine based non-relevance feedback document retrieval. 552-557 - Taeho Jo, Nathalie Japkowicz:
Text clustering with NTSO (neural text self organizer). 558-563 - Vikramjit Mitra, Chia-Jiu Wang, Satarupa Banerjee:
A neuro-SVM model for text classification using latent semantic indexing. 564-569 - Feng-Yao Liu, Ke Wu, Hai Zhao, Bao-Liang Lu:
Fast text categorization with min-max modular support vector machines. 570-575 - Kazumi Saito, Ryohei Nakano:
Weight sharing on naive Bayes document model. 576-581 - John Zakos, Ping Zhang, Brijesh K. Verma:
Optimization of parameters for effective Web information retrieval using an evolutionary algorithm. 582-587 - Sin Wee Lee, Dominic Palmer-Brown:
Snap-drift learning for phrase recognition. 588-592 - Miao Kang, Dominic Palmer-Brown:
An adaptive function neural network (ADFUNN) for phrase recognition. 593-597 - Mussa Abdella, Tshilidzi Marwala:
Treatment of missing data using neural networks and genetic algorithms. 598-603 - Saravanakumar Srinivasan, Mahmood R. Azimi-Sadjadi:
An iterative relevance feedback learning algorithm for image retrieval systems. 604-609 - Arthur Steven Younger, Emmett Redd:
Design of an optical fixed-weight learning neural network. 610-615 - Richard B. Wells, Anindya Bhattacharya, Ben Sharon, Priyank Gupta, Sam Young, Sanjeev Giri, Terseer Ityavyar, Dave Cox:
Forgetful logic circuits for pulse-mode neural networks. 616-621 - Zhongwen Luo, Hongzhi Liu, Xincai Wu:
Artificial neural network computation on graphic process unit. 622-626 - Xiaolei Zhu, Jizhong Shen, Baoyong Chi, Zhihua Wang:
Circuit implementation of multi-thresholded neuron (MTN) using BiCMOS technology. 627-632 - Yutaka Maeda, Makito Nakatsuka:
FPGA implementation of pulse coupled oscillator. 633-638 - Kazuki Nakada, Tetsuya Asai, Tetsuya Hirose, Yoshihito Amemiya:
Analog current-mode CMOS implementation of central pattern generator for robot locomotion. 639-644 - Yili Quan, Albert H. Titus:
Toward an analog VLSI implementation of a decision making model. 645-650 - Valeriu Bern, Artur Zawadzki:
On Kolmogorov's superpositions: novel gates and circuits for nanoelectronics? 651-656 - Qiuye Zhao, Donald S. Reay:
Hardware implementation of CMAC and B-spline neural networks for embedded applications. 657-662 - Paolo Gastaldo, Giovanni Parodi, Rodolfo Zunino:
DSP-based neural systems for the perceptual assessment of visual quality. 663-668 - Davide Piombo, Rodolfo Zunino:
Analog current-mode design for soft-max computation. 669-674 - Davide Piombo, Rodolfo Zunino:
Analog current-mode design for soft-max computation. 669-674 - Øivind Næss, Snorre Aunet, Yngvar Berg:
Low-voltage pseudo floating-gate reconfigurable linear threshold elements. 675-680 - Davide Anguita, Giovanni Bozza:
The effect of quantization on support vector machines with Gaussian kernel. 681-684 - Hugo de Garis, Wang Ce, Thayne Batty:
Building a cheaper artificial brain. 685-688 - Teppei Nakano, Takashi Morie:
A digital LSI architecture of elastic graph matching and its FPGA implementation. 689-694 - Jeffrey Dungen, Jean-Jules Brault:
Simulated control of a tracking mobile robot by four aVLSI integrate-and-fire neurons paired into maps. 695-699 - Wen Yu, José de Jesús Rubio, Xiaoou Li:
Recurrent neural networks training with stable risk-sensitive Kalman filter algorithm. 700-705 - Yasuaki Kuroe, Kei Miura:
A method of oscillatory trajectory generation using recurrent hybrid neural networks. 706-711 - Shubao Liu, Jun Wang:
A new k-winners-take-all neural network. 712-716 - Paris A. Mastorocostas:
A constrained optimization algorithm for training locally recurrent globally feedforward neural networks. 717-722 - Sylvain Chartier, Sébastien Hélie, Mounir Boukadoum, Robert Proulx:
SCRAM: statistically converging recurrent associative memory. 723-728 - Marco Gori, Gabriele Monfardini, Franco Scarselli:
A new model for learning in graph domains. 729-734 - Gürsel Serpen:
Empirical approximation for Lyapunov functions with artificial neural nets. 735-740 - Luciana P. P. Bueno, Aluízio F. R. Araújo:
Ability to skip steps emerging from chaotic dynamics. 741-746 - Yoshitsugu Kakemoto, Shinichi Nakasuka:
Nonlinear dynamics on VSF-network. 747-752 - Toshijiro Tanaka, Etsumasa Hiura:
Dynamical behavior of a chaotic neural and its application to optimization problems. 753-757 - Masaharu Adachi:
An analysis of associative chaotic neurodynamics by using surrogate neurons. 758-763 - Xiaofei Huang:
A new kind of Hopfield networks for finding global optimum. 764-769 - Colin Molter, Utku Salihoglu, Hugues Bersini:
Learning cycles brings chaos in Hopfield networks. 770-775 - Filip Piekniewski, Tomasz Schreiber:
Phase diagrams for locally Hopfield neural networks in presence of correlated patterns. 776-781 - Ikuko Nishikawa, Kazutoshi Sakakibara, Takeshi Iritani, Yasuaki Kuroe:
2 types of complex-valued Hopfield networks and the application to a traffic signal control. 782-787 - Thanh-Nhat Le, Cong-Kha Pham:
A new N-parallel updating method of the Hopfield-type neural network for N-queens problem. 788-791