14.5.5.2 Neural Networks Combinations and Evaluations

Hansen, L.K., Salamon, P.,
Neural network ensembles,
PAMI(12), No. 10, October 1990, pp. 993-1001.
IEEE Abstract. IEEE Top Reference.
WWW Version. 0401 BibRef

Bourlard, H., Wellekens, C.J.,
Links between Markov models and multilayer perceptrons,
PAMI(12), No. 12, December 1990, pp. 1167-1178.
IEEE Abstract. IEEE Top Reference.
WWW Version. 0401 BibRef

Fu, L.M.[Li-Min],
Analysis of the dimensionality of neural networks for pattern recognition,
PR(23), No. 10, 1990, pp. 1131-1140.
WWW Version. 0401 BibRef

Musavi, M.T., Chan, K.H., Hummels, D.M., Kalantri, K., Ahmed, W.,
A probabilistic model for evaluation of neural network classifiers,
PR(25), No. 10, October 1992, pp. 1241-1251.
WWW Version. 0401 BibRef

Ruck, D.W., Rogers, S.K., Kabrisky, M., Maybeck, P.S., Oxley, M.E.,
Comparative analysis of backpropagation and the extended Kalman filter for training multilayer perceptrons,
PAMI(14), No. 6, June 1992, pp. 686-691.
IEEE Abstract. IEEE Top Reference.
WWW Version. 0401 BibRef

Schmidt, W.A.C., Davis, J.P.,
Pattern recognition properties of various feature spaces for higher order neural networks,
PAMI(15), No. 8, August 1993, pp. 795-801.
IEEE Abstract. IEEE Top Reference.
WWW Version. 0401 BibRef

Jou, I.C.[I. Chang], You, S.S.[Shih-Shien], Chang, L.W.[Long-Wen],
Analysis of hidden nodes for multi-layer perceptron neural networks,
PR(27), No. 6, June 1994, pp. 859-864.
WWW Version. 0401 BibRef

Hamamoto, Y., Uchimura, S., Tomita, S.,
On the Behavior of Artificial Neural-Network Classifiers in High-Dimensional Spaces,
PAMI(18), No. 5, May 1996, pp. 571-574.
IEEE Abstract. IEEE Top Reference.
WWW Version. 9606 Neural Networks. BibRef

Hamamoto, Y., Matsuura, Y., Kanaoka, T., and Tomita, S.,
A Note on the Orthonormal Discriminant Vector Method for Feature Extraction,
PR(24), No. 7, 1991, pp. 681-684.
WWW Version. BibRef 9100

Hamamoto, Y.[Yoshihiko], Kanaoka, T.[Taiho], Tomita, S.[Shingo],
On a theoretical comparison between the orthonormal discriminant vector method and discriminant analysis,
PR(26), No. 12, December 1993, pp. 1863-1867.
WWW Version. 0401 BibRef
Earlier:
Orthogonal discriminant analysis for interactive pattern analysis,
ICPR90(I: 424-427).
WWW Version. 9006 BibRef

Hamamoto, Y., Ohama, A., Kanaoka, T., Tomita, S.,
Orthogonal discriminant analysis based on a modified Fisher criterion,
ICPR92(II:363-366).
WWW Version. 9208feature extraction BibRef

Archer, N.P., Wang, S.,
Learning bias in neural networks and an approach to controlling its effect in monotonic classification,
PAMI(15), No. 9, September 1993, pp. 962-966.
IEEE Abstract. IEEE Top Reference.
WWW Version. 0401 BibRef

Park, Y.T.[Young-Tae],
A comparison of neural net classifiers and linear tree classifiers: Their similarities and differences,
PR(27), No. 11, November 1994, pp. 1493-1503.
WWW Version. 0401 BibRef

Sarat Chandran, P.,
Comments on 'Comparative analysis of backpropagation and the extended Kalman filter for training multilayer perceptrons',
PAMI(16), No. 8, August 1994, pp. 862-863.
IEEE Abstract. IEEE Top Reference.
WWW Version. 0401 See also Comparative analysis of backpropagation and the extended Kalman filter for training multilayer perceptrons. BibRef

Cho, S.B., Kim, J.H.,
Combining Multiple Neural Netowrks by Fuzzy Integral for Robust Classification,
SMC(25), No. 2, 1995, pp. 380-384. BibRef 9500

Cho, S.B., Kim, J.H.,
Multiple Network Fusion Using Fuzzy Logic,
TNN(6), No. 2, 1995, pp. 497-501. BibRef 9500

Hashem, Schmeiser, B.,
Improving Model Accuracy Using Optimal Linear Combinations of Trained Neural Networks,
TNN(6), No. 3, 1995, pp. 792-794. BibRef 9500

Chong, C.C., Jia, J.C.,
Assessments of Neural-Network Classifier Output Codings Using Variability of Hamming Distance,
PRL(17), No. 8, July 1 1996, pp. 811-818. 9608 BibRef
Earlier:
Assessments of neural network output codings for classification of multispectral images using Hamming distance measure,
ICPR94(B:526-528).
WWW Version. 9410 BibRef

Kanellopoulos, I., Wilkinson, G.G.,
Strategies and Best Practice for Neural-Network Image Classification,
JRS(18), No. 4, March 10 1997, pp. 711-725. 9703 BibRef

Holmstrom, L., Koistinen, P., Laaksonen, J.T., Oja, E.,
Neural and Statistical Classifiers: Taxonomy and Two Case-Studies,
TNN(8), No. 1, January 1997, pp. 5-17. 9701 BibRef

Guan, L., Anderson, J.A., Sutton, J.P.,
A Network of Networks Processing Model for Image Regularization,
TNN(8), No. 1, January 1997, pp. 169-174. 9701 BibRef

Gao, D.Q., Wu, S.Y.,
An Optimization Method for the Topological Structures of Feedforward Multilayer Neural Networks,
PR(31), No. 9, September 1998, pp. 1337-1342.
WWW Version. 9808 BibRef

Tang, X.O.,
Multiple Competitive Learning Network Fusion for Object Classification,
SMC-B(28), No. 4, August 1998, pp. 532-543.
IEEE Top Reference. 9808 BibRef

Babri, H.A., Chen, Y.Q., Yin, T.,
Improving Backpropagation Learning Under Limited Precision,
PRL(19), No. 11, September 1998, pp. 1007-1016. 9811 BibRef

Sierra, A., Santa Cruz, C.,
Global and Local Neural-Network Ensembles,
PRL(19), No. 8, June 1998, pp. 651-655. 9808 BibRef

Gori, M., Tesi, A.,
On the problem of local minima in backpropagation,
PAMI(14), No. 1, January 1992, pp. 76-86.
IEEE Abstract. IEEE Top Reference.
WWW Version. 0401 BibRef

Venkatesh, S.S., Psaltis, D.,
On reliable computation with formal neurons,
PAMI(14), No. 1, January 1992, pp. 87-91.
IEEE Abstract. IEEE Top Reference.
WWW Version. 0401 BibRef

Gori, M.[Marco], Scarselli, F.[Franco],
Are Multilayer Perceptrons Adequate for Pattern-Recognition and Verification,
PAMI(20), No. 11, November 1998, pp. 1121-1132.
IEEE Abstract. IEEE Top Reference.
WWW Version. 9811 BibRef

Lerner, B.[Boaz], Guterman, H.[Hugo], Aladjem, M.[Mayer], Dinstein, I.[Its'hak],
Comparative Study of Neural Network Based Feature Extraction Paradigms,
PRL(20), No. 1, January 1999, pp. 7-14. BibRef 9901
Earlier:
Feature Extraction by Neural Network Nonlinear Mapping for Pattern Classification,
ICPR96(IV: 320-324).
WWW Version. 9608(Ben-Gurion Univ. IL) BibRef

Serpico, S.B., Bruzzone, L., Roli, F.,
An Experimental Comparison of Neural and Statistical Nonparametric Algorithms for Supervised Classification of Remote Sensing Images,
PRL(17), No. 13, November 25 1996, pp. 1331-1341. 9701 Neural Networks. Comparisons. Remote Sensing. BibRef

Giacinto, G.[Giorgio], Roli, F.[Fabio], Bruzzone, L.[Lorenzo],
Combination of neural and statistical algorithms for supervised classification of remote-sensing images,
PRL(21), No. 5, May 2000, pp. 385-397. 0005 See also approach to the automatic design of multiple classifier systems, An. BibRef

Bruzzone, L., Fernàndez Prieto, D., Serpico, S.B.,
A Neural-Statistical Approach to Multitemporal and Multisource Remote-Sensing Image Classification,
GeoRS(37), No. 3, May 1999, pp. 1350.
IEEE Top Reference. BibRef 9905

Roli, F., Serpico, S., Bruzzone, L.,
Classification of Multisensor Remote Sensing Images by Multiple Structured Neural Networks,
ICPR96(IV: 180-184).
WWW Version. 9608(Univ. di Cagliari, I) BibRef

Bruzzone, L., Serpico, S.B.,
Classification of Imbalanced Remote Sensing Data by Neural Networks,
PRL(18), No. 11-13, November 1997, pp. 1323-1328. 9806 BibRef

Bruzzone, L., Fernàndez Prieto, D.,
An incremental-learning neural network for the classification of remote-sensing images,
PRL(20), No. 11-13, November 1999, pp. 1241-1248. 0001 BibRef

Bruzzone, L., Carlin, L.,
A Multilevel Context-Based System for Classification of Very High Spatial Resolution Images,
GeoRS(44), No. 9, September 2006, pp. 2587-2600.
WWW Version. 0609 BibRef

Baraldi, A., Puzzolo, V., Blonda, P., Bruzzone, L., Tarantino, C.,
Automatic Spectral Rule-Based Preliminary Mapping of Calibrated Landsat TM and ETM+ Images,
GeoRS(44), No. 9, September 2006, pp. 2563-2586.
WWW Version. 0609 BibRef

Perner, P.[Petra], Zscherpel, U.[Uwe], Jacobsen, C.[Carsten],
A comparison between neural networks and decision trees based on data from industrial radiographic testing,
PRL(22), No. 1, January 2001, pp. 47-54.
HTML Version. 0105 BibRef

Baraldi, A., Binaghi, E., Blonda, P., Brivio, P.A., Rampini, A.,
Comparison of the multilayer perceptron with neuro-fuzzy techniques in the estimation of cover class mixture in remotely sensed data,
GeoRS(39), No. 5, May 2001, pp. 994-1005.
IEEE Top Reference. 0106 BibRef

Giacinto, G.[Giorgio], Roli, F.[Fabio],
Design of effective neural network ensembles for image classification purposes,
IVC(19), No. 9-10, August 2001, pp. 699-707.
WWW Version. 0108 See also Combination of neural and statistical algorithms for supervised classification of remote-sensing images. BibRef

Giacinto, G., Roli, F., Fumera, G.,
Design of Effective Multiple Classifier Systems by Clustering of Classifiers,
ICPR00(Vol II: 160-163).
WWW Version.
HTML Version. 0009 BibRef

Hinton, G.E.[Geoffrey E.],
Training products of experts by minimizing contrastive divergence,
NeurComp(14), No. 8, 2002, pp. 1771-1800.
WWW Version. BibRef 0200

Behloul, F., Lelieveldt, B.P.F., Boudraa, A., Reiber, J.H.C.,
Optimal design of radial basis function neural networks for fuzzy-rule extraction in high dimensional data,
PR(35), No. 3, March 2002, pp. 659-675.
WWW Version. 0201 BibRef

Woods, K., Bowyer, K.W.,
Generating ROC curves for artificial neural networks,
MedImg(16), No. 3, June 1997, pp. 329-337.
IEEE Top Reference. 0205 BibRef

Gupta, L.[Lalit], McAvoy, M.[Mark],
Investigating the prediction capabilities of the simple recurrent neural network on real temporal sequences,
PR(33), No. 12, December 2000, pp. 2075-2081.
WWW Version. 0008 BibRef

Racca, R.[Robert],
Can periodic perceptrons replace multi-layer perceptrons?,
PRL(21), No. 12, November 2000, pp. 1019-1025. 0011 BibRef

Pal, N.R., Bezdek, J.C.,
Complexity reduction for 'large image' processing,
SMC-B(32), No. 5, October 2002, pp. 598-611. Sampling method. Train NN or clustering on the samples only.
IEEE Top Reference. 0210 BibRef

Jiang, Y.[Yulei],
Uncertainty in the output of artificial neural networks,
MedImg(22), No. 7, July 2003, pp. 913-921.
IEEE Abstract. IEEE Top Reference. 0308 BibRef

Weber, K.E.[Karsten E.], Schlagner, W.[Werner], Schweier, K.[Knuth],
Estimating regional noise on neural network predictions,
PR(36No. 10, October 2003, pp. 2333-2337.
WWW Version. 0308 BibRef

Daqi, G.[Gao], Genxing, Y.[Yang],
Influences of variable scales and activation functions on the performances of multilayer feedforward neural networks,
PR(36), No. 4, April 2003, pp. 869-878.
WWW Version. 0304 BibRef

Abbas, H.M.,
Analysis and pruning of nonlinear auto-association networks,
VISP(151), No. 1, February 2004, pp. 44-50.
IEEE Abstract. IEEE Top Reference. 0403Analysis of Neural networks. BibRef

Fernandes, A.M.[Armando M.], Utkin, A.B.[Andrei B.], Lavrov, A.V.[Alexander V.], Vilar, R.M.[Rui M.],
Development of neural network committee machines for automatic forest fire detection using lidar,
PR(37), No. 10, October 2004, pp. 2039-2047.
WWW Version. 0409 BibRef

Fernandes, A.M.[Armando M.], Utkin, A.B.[Andrei B.], Lavrov, A.V.[Alexander V.], Vilar, R.M.[Rui M.],
Design of committee machines for classification of single-wavelength lidar signals applied to early forest fire detection,
PRL(26), No. 5, April 2005, pp. 625-632.
WWW Version. 0501 BibRef

Daqi, G.[Gao], Yan, J.[Ji],
Classification methodologies of multilayer perceptrons with sigmoid activation functions,
PR(38), No. 10, October 2005, pp. 1469-1482.
WWW Version. 0508 BibRef

Cantu-Paz, E., Kamath, C.,
An empirical comparison of combinations of evolutionary algorithms and neural networks for classification problems,
SMC-B(35), No. 5, October 2005, pp. 915-927.
WWW Version. 0510Compares 8 combinations of NN algorithms. BibRef

Hervás-Martínez, C.[César], Martínez-Estudillo, F.[Francisco],
Logistic regression using covariates obtained by product-unit neural network models,
PR(40), No. 1, January 2007, pp. 52-64.
WWW Version. 0611Logistic regression; Product-unit neural network; Classification BibRef

Gao, D.[Daqi], Li, C.X.[Chun-Xia], Yang, Y.[Yunfan],
Task decomposition and modular single-hidden-layer perceptron classifiers for multi-class learning problems,
PR(40), No. 8, August 2007, pp. 2226-2236.
WWW Version. 0704Task decomposition; Multi-class learning data sets; Modular multilayer perceptrons; Unbalanced classes; Weak distribution regions; Output amendment BibRef

Saavedra, C.[Carolina], Moreno, S.[Sebastián], Salas, R.[Rodrigo], Allende, H.[Héctor],
Robustness Analysis of the Neural Gas Learning Algorithm,
CIARP06(559-568).
WWW Version. 0611 BibRef

Sridharan, K.[Karthik], Beal, M.J.[Matthew J.], Govindaraju, V.[Venu],
Competitive Mixtures of Simple Neurons,
ICPR06(II: 494-497).
WWW Version. 0609 BibRef

Lefebvre, G.[Gregoire], Laurent, C.[Christophe], Ros, J.[Julien], Garcia, C.[Christophe],
Supervised Image Classification by SOM Activity Map Comparison,
ICPR06(II: 728-731).
WWW Version. 0609 BibRef

Ros, J.[Julien], Laurent, C.[Christophe], Lefebvre, G.[Grégoire],
A Cascade of Unsupervised and Supervised Neural Networks for Natural Image Classification,
CIVR06(92-101).
WWW Version. 0607 BibRef

Calitoiu, D.[Dragos], Oommen, B.J.[B. John], Nusbaumm, D.[Dorin],
Modeling Inaccurate Perception: Desynchronization Issues of a Chaotic Pattern Recognition Neural Network,
SCIA05(821-830).
WWW Version. 0506 BibRef

Adeodato, P.J.L., Vasconcelos, G.C., Arnaud, A.L., Santos, R.A.F., Cunha, R.C.L.V., Monteiro, D.S.M.P.,
Neural Networks vs. Logistic Regression: A Comparative Study on a Large Data Set,
ICPR04(III: 355-358).
WWW Version. 0409 BibRef

Steinkraus, D., Buck, I., Simard, P.Y.,
Using GPUs for machine learning algorithms,
ICDAR05(II: 1115-1120).
WWW Version. 0508 BibRef

Simard, P.Y., Steinkraus, D., Platt, J.C.,
Best practices for convolutional neural networks applied to visual document analysis,
ICDAR03(958-963).
IEEE Abstract. IEEE Top Reference. 0311 BibRef

Doering, A., Witte, H.,
Feedforward Neural Networks for Bayes-Optimal Classification: Investigations on the Influence of the Composition of the Training Set on the Cost Function,
ICPR96(IV: 219-223).
WWW Version. 9608(Klinikum der Friedrich Schiller-Univ. D) BibRef

Holt, M.J.J.,
Comparison of generalization in multi-layer perceptrons with the log-likelihood and least-squares cost functions,
ICPR92(II:17-20).
WWW Version. 9208 BibRef

Chen, C.H.,
A comparison of neural network models for pattern recognition,
ICPR90(II: 45-46).
WWW Version. 9208 BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Neural Networks for Classification and Pattern Recognition .