GüREŞEN–kayakutlu 1 kho biLİm dergiSİ CİLT: 23 sayi: yil: 2013
Yüklə 411,56 Kb. Pdf görüntüsü
|
GÜREŞEN–KAYAKUTLU
90 KHO BİLİM DERGİSİ CİLT: 23 SAYI: 1 YIL: 2013
Yapay zeka çalışmalarının insan gibi düşünebilen yapıya yaklaşabilmeleri için aşmaları gereken ilk problem, yapay zeka modellerinin hangi yeni işlevleri veya özellikleri kazanması gerektiğidir. İkinci temel problem, bu yeni işlev ve özellik kazanma işlemlerinin yapılması için gerekli koşulların tespitidir. Üçüncüsü ve sonuncusu ise yeni işlev ve özellik kazanma işlemlerinin ne şekilde yapılacağıdır. Yapay zeka önünde duran bu üç temel engeli “neyi”, “ne zaman” ve “nasıl” olarak kısaca özetleyebiliriz. Bu çalışma “nasıl” sorusunu sorarak, üçüncü temel engelin aşılması için sade bir çözüm getirmiştir. Bu noktadan sonra yapılabilecek yapay zeka çalışmaları “nasıl” sorusuna cevap arayarak öğrenme kavramını geliştirecek yeni yöntemler geliştirebilirler veya bir sonraki aşamaya geçerek “ne zaman” veya “neyi” sorusuna cevap arayabilirler.
GÜREŞEN–KAYAKUTLU
91 KHO BİLİM DERGİSİ CİLT: 23 SAYI: 1 YIL: 2013 Kaynakça (References)
Alpaydın, E., 2010. Introduction to Machine Learning. The MIT Press, London, England.
Ash, T., 1989. "Dynamic node creation in backpropagation networks". Connection Science, 1(4): 365-375.
Azimi-Sadjadi, M. R., Sheedvash, S. ve Trujillo, F. O., 1993. "Recursive Dynamic Node Creation". IEEE TRANSACTIONS ON NEURAL NETWORKS, 4(2): 242-256.
Back, T. ve Kursawe, F.,1995. “Evolutionary algorithms for fuzzy logic: A brief overview”. Fuzzy logic and soft computing (Ed.). World Scientific, Singapore, (Sf.3-10).
Broomhead, D. S. ve Lowe, D., 1988. "Multivariable Functional Interpolation and Adaptive Networks". Complex Systems, 2: 321-355.
Buntine, W. L. ve Weigend, A. S., 1991. "Bayesian backpropagation". Complex Systems, 5: 603–643.
Burkitt, A. N., 1991. "Optimization of the architecture of feed-forward neural networks with hidden layers by unit elimination". Complex Systems, 5: 371-380.
Castellano, G., Fanelli, A. M. ve Pelillo, M., 1993. “An Empirical Comparison of Node Pruning Methods for Layered Feed-forward Neural Networks”. International Joint Conference on Neural Networks. Nagoya, Japan. 1: 321-326.
Castellano, G., Fanelli, A. M. ve Pelillo, M., 1997. "An Iterative Pruning Algorithm for Feedforward Neural Networks". IEEE TRANSACTIONS ON NEURAL NETWORKS, 8(3): 519-531.
GÜREŞEN–KAYAKUTLU
92 KHO BİLİM DERGİSİ CİLT: 23 SAYI: 1 YIL: 2013 Castillo, P. A., Merelo, J. J., Prieto, A., Rivas, V. ve Romero, G., 2000. "G- Prop: Global optimization of multilayer perceptrons using GAs".
Çınar, D., 2007. Hidroelektrik Enerji Üretiminin Hibrid Bir Model ile Tahmini. Industrial Engineering. Istanbul, Istanbul Teknik Üniversitesi. M.S.: 108.
Doganis, P., Alexandridis, A., Patrinos, P. ve Sarimveis, H., 2006. "Time series sales forecasting for short shelf-life food products based on artificial neural networks and evolutionary computing". Journal of Food Engineering, 75: 196-204.
Fahlman, S. E. ve Lebiere, C.,1990. The cascade-correlation learning architecture. Advances in Neural Information Processing Systems. Touretzky, D. S. (Ed.). Morgan Kaufmann, San Mateo, CA, (Sf.524– 532).
in knowledge discovery and data mining. MIT Press, Cambridge, MA.
Ferentinos, K. P., 2005. "Biological engineering applications of feedforward neural networks designed and parameterized by genetic algorithms".
Frattale-Mascioli, F. M. ve Martinelli, G., 1995. "A constructive algorithm for binary neural networks: the oil-spot algorithm". IEEE Transactions on Neural Networks, 6(3): 794 - 797
Frean, M., 1990. "The Upstart Algorithm a method for constructing and training feedforward neural networks". Neural Computation,2: 198-209.
Friedman, J. H. ve Stuetzle, W., 1981. "Projection Pursuit Regression". Journal of the American Statistical Association, 73(376): 817-823.
GÜREŞEN–KAYAKUTLU
93 KHO BİLİM DERGİSİ CİLT: 23 SAYI: 1 YIL: 2013 Fritzke, B., 1993. Growing cell structures--A self-organizing network for unsupervised and supervised learning, International Computer Science Institution.
Fritzke, B., 1994a. "Growing cell structures--A self-organizing network for unsupervised and supervised learning". Neural Networks: 1441-1460.
Fritzke, B.,1994b. Supervised Learning with Growing Cell Structures. Advances in Neural Information Processing Systems Cowan, J. D., Tesauro, G. ve Alspector, J. (Ed.). Morgan Kaufmann, San Mateo, CA, USA, (Sf.255-262).
Fritzke, B., 1995a. "Growing Grid - a self - organizing network with constantneighborhood range and adaptation strength". Neural Processing Letters, 2(5): 9-13. Fritzke, B.,1995b. A Growing Neural Gas Network Learns Topologies. Advances in Neural Information Processing Systems. Tesauro, G., Touretzky, D. S. ve Leen, T. K. (Ed.). MIT Press, Cambridge, MA).
Gallant, S. I., 1986. Optimal Linear Discriminants. IEEE 8th Conferance on Pattern Recognition. Paris, France.
Gallant, S. I., 1990. "Perceptron-based learning algorithms". IEEE Transactions on Neural Networks, 1(2): 179-191.
Ghiassi, M. ve Saidane, H., 2005. "A dynamic architecture for artificial neural networks". Neurocomputing, 63: 397-413.
Goldberg, D., 1989. Genetic algorithms in search, optimization and machine learning. Addison-Wesley, Reading, MA.
Han, H.-G. ve Qiao, J.-F., 2013. "A structure optimisation algorithm for feedforward neural network construction". Neurocomputing, 99: 347- 357.
Hwang, J.-N., Lay, S.-R., Maechler, M., Martin, R. D. ve Schimert, J., 1994. "Regression Modeling in Back-Propagation and Proiection Pursuit
GÜREŞEN–KAYAKUTLU
94 KHO BİLİM DERGİSİ CİLT: 23 SAYI: 1 YIL: 2013 Learning ". IEEE TRANSACTIONS ON NEURAL NETWORKS, 5(3): 342-353.
Ivakhnenko, A. G.,1984. Self-Organizing Methods in Modeling: GMDH Type Algorithms. Statistics: Textbooks and Monographs. Farlow, S. J. (Ed.). Marcel Dekker Inc., New York:).
Karnin, E. D., 1990. "A simple procedure for pruning back-propagation trained neural networks". IEEE Transactions on Neural Networks, 1(2): 239– 242.
Kennedy, J. ve Eberhart, R., 1995. "Particle swarm optimization." IEEE International Conferance on Neural Networks, Perth, Australia.
Kim, B. ve Bae, J., 2005. "Prediction of plasma processes using neural network and genetic algorithms". Solid-State Electronics, 49: 1576- 1580.
Kiranyaz, S., Ince, T., Yildirim, A. ve Gabbouj, M., 2009. "Evolutionary artificial neural networks by multi-dimensional particle swarm optimization".
Koza, J., 1992. Genetic programming: On the programming of computers by means of natural selection. MIT Press, Cambridge, MA.
Kuo, R. J., 2001. "A sales forecasting system based on fuzzy neural network with initial weights generated by genetic algorithm". European Journal of Operational Research, 129: 496-517.
Kwok, T.-Y. ve Yeung, D.-Y., 1997a. "Constructive Algorithms for Structure Learning in Feedforward Neural Networks for Regression Problems". IEEE TRANSACTIONS ON NEURAL NETWORKS, 8(3): 630-645.
Kwok, T.-Y. ve Yeung, D.-Y., 1997b. "Objective Functions for Training New Hidden Units in Constructive Neural Networks". IEEE TRANSACTIONS ON NEURAL NETWORKS, 8(5): 1131-1148
GÜREŞEN–KAYAKUTLU
95 KHO BİLİM DERGİSİ CİLT: 23 SAYI: 1 YIL: 2013 Li, Z., Cheng, G. ve Qiang, X., 2010. Some Classical Constructive Neural Networks and their New Developments. 2010 International Conference on Educational and Network Technology (ICENT 2010). Qinhuangdao, China 174-178.
Ma, L. ve Khorasani, K., 2003. "A new strategy for adaptively constructing multilayer feedforward neural networks". Neurocomputing, 51: 361- 385.
Mezard, M. ve Nadal, J.-p., 1989. "Learning in feedforward layered networks: The tiling algorithm". Journal of Physics A: Math. Gen, 22(12): 2191- 2203.
Moody, J. ve Darken, C., 1989. "Fast Learning in Networks of Locally-Tuned Processing Units". Neural Computation, 1: 281-294.
Mozer, M. C. ve Smolensky, P.,1989. Skeletonization: a technique for trimming the fat from a network via relevance assessment. Advances in Neural Information Processing Touretzky, D. S. (Ed.). Morgan Kaufman, San Mateo, CA, (Sf.107–115).
Ngom, A., Stojmenovic, I. ve Milutinovic, V., 2001. "STRIP—A Strip-Based Neural-Network Growth Algorithm for Learning Multiple-Valued Functions". IEEE TRANSACTIONS ON NEURAL NETWORKS, 12(2): 212-227.
Niska, H., Hiltunen, T., Karppinen, A., Ruuskanen, J. ve Kolehmainen, M., 2004. "Evolving the neural network model for forecasting air pollution time series". Engineering Applications of Artificial Intelligence, 17: 159-167. Parekh, R., Yang, J. ve Honavar, V., 2000. "Constructive Neural-Network Learning Algorithms for Pattern Classification". IEEE TRANSACTIONS
Parker, R. E. ve Tummala, M., 1992. “Identification of Volterra systems with a polynomial neural network”.IEEE International Conference on GÜREŞEN–KAYAKUTLU
96 KHO BİLİM DERGİSİ CİLT: 23 SAYI: 1 YIL: 2013 Acoustics, Speech, and Signal Processing. San Francisco, CA, USA 561–564.
Pelillo, M. ve Fanelli, A. M.,1993. A method of pruning layered feed-forward neural networks. New Trends in Neural Computation (Lecture Notes in Computer Science). Mira, J., Cabestany, J. ve Prieto, A. (Ed.). Springer-Verlag, Berlin, (Sf.278-283).
Phatak, D. S. ve Koren, I., 1994 "Connectivity and performance tradeoffs in the cascade correlation learning architecture". IEEE Transactions on Neural Networks, 5(6): 1045-9227
Platt, J., 1991. "A Resource-Allocating Network for Function Interpolation". Neural Computation, 3(2): 213-225.
Puma-Villanueva, W. J., dos Santos, E. P. ve Von Zuben, F. J., 2012. "A constructive algorithm to synthesize arbitrarily connected feedforward neural networks". Neurocomputing, 75(1): 14-32.
Reilly, D. L., Cooper, L. N. ve Elbaum, C., 1982. " A neural model for category learning". Biological Cybernetics, 45: 35-41.
Saha, A., Wu, C.-L. ve Tang, D.-S., 1993. "Approximation, Dimension Reduction, and Nonconvex ODtimization Using Linear Superpositions of Gaussians". IEEE TRANSACTIONS ON COMPUTERS, 42(10): 1222-1233.
Setiono, R., 2001. "Feedforward Neural Network Construction Using Cross Validation". Neural Computation, 13: 2865-2877.
Sexton, R. S., Dorsey, R. E. ve Sikander, N. A., 2004. "Simultaneous optimization of neural network function and architecture algorithm". Decision Support Systems, 36: 283-296.
Shin, Y. ve Ghosh, J., 1995. "Ridge Polynomial Networks". IEEE TRANSACTIONS ON NEURAL NETWORKS, 6(3): 610-622. GÜREŞEN–KAYAKUTLU
97 KHO BİLİM DERGİSİ CİLT: 23 SAYI: 1 YIL: 2013 Sietsma, J. ve Dow, R. J. F., 1988. Neural net pruning - Why and how. International Conferance on Neural Networks. San Diego, CA, USA: 325-333.
Sjogaard, S., 1992. Generalization in cascade-correlation networks. Neural Networks for Signal Processing. Helsingoer, Denmark 59 - 68.
Subirats, J. L., Franco, L. ve Jerez, J. M., 2012. "C-Mantec: A novel constructive neural network algorithm incorporating competition between neurons". Neural Networks, 26: 130–140.
Tenorio, M. F. ve Lee, W. T., 1990. "Self-organizing network for optimum supervised learning". IEEE Transactions on Neural Networks, 1(1): 100-110.
Thodberg, H. H., 1996. "A review of Bayesian neural networks with an application to near infrared spectroscopy". IEEE Transactions on Neural Networks, 7: 56–72.
Verkooijen, W. ve Daniels, H., 1994. "Connectionist Projection Pursuit Regression". Computational Economics, 7: 155-161.
Wang, D., 2008. "Fast Constructive-Covering Algorithm for neural networks and its implement in classification". Applied Soft Computing, 8(1): 166-173.
Wang, T. ve Huang, C., 2007. "Applying optimized BPN to a chaotic time series problem". Expert Systems with Applications, 32: 193-200.
Yang, S.-H. ve Chen, Y.-P., 2012. "An evolutionary constructive and pruning algorithm for artificial neural networks and its prediction applications". Neurocomputing, 86: 140-149.
Yi-Hui, L., 2007. "Evolutionary neural network modeling for forecasting the field failure data of repairable systems". Expert Systems with Applications, 33: 1090-1096.
GÜREŞEN–KAYAKUTLU
98 KHO BİLİM DERGİSİ CİLT: 23 SAYI: 1 YIL: 2013 Young, S. ve Downs, T., 1998. "CARVE - A Constructive Algorithm for Real Valued Examples". IEEE Transactions on Neural Networks, 9(6): 1180-1190.
Zhang, B.-T., 1994. An incremental learning algorithm that optimizes network size and sample size in one trial. IEEE World Congress on Neural Networks. Orlando, Florida. 1: 215-220.
Yüklə 411,56 Kb. Dostları ilə paylaş:
|