Handbook on Neural Information Processing

1. Deep Learning of Representations
Yoshua Bengio, Aaron Courville

2. Recurrent Neural Networks
Sajid A. Marhon, Christopher J. F. Cameron, Stefan C. Kremer

3. Supervised Neural Network Models for Processing Graphs
Monica Bianchini, Marco Maggini

4. Topics on Cellular Neural Networks
Liviu Goraş, Ion Vornicu, Paul Ungureanu

5. Approximating Multivariable Functions by Feedforward Neural Nets
Paul C. Kainen, Věra Kůrková, Marcello Sanguineti

6. Bochner Integrals and Neural Networks
Paul C. Kainen, Andrew Vogt

7. Semi-supervised Learning
Mohamed Farouk Abdel Hady, Friedhelm Schwenker

8. Statistical Relational Learning
Hendrik Blockeel

9. Kernel Methods for Structured Data
Andrea Passerini

10. Multiple Classifier Systems: Theory, Applications and Tools
Francesco Gargiulo, Claudio Mazzariello, Carlo Sansone

11. Self Organisation and Modal Learning: Algorithms and Applications
Dominic Palmer-Brown, Chrisina Jayne

12. Bayesian Networks, Introduction and Practical Applications
Wim Wiegerinck, Willem Burgers, Bert Kappen

13. Relevance Feedback in Content-Based Image Retrieval: A Survey
Jing Li, Nigel M. Allinson

14. Learning Structural Representations of Text Documents in Large Document Collections
Ah Chung Tsoi, Markus Hagenbuchner, Milly Kc, ShuJia Zhang

15. Neural Networks in Bioinformatics
Masood Zamani, Stefan C. Kremer

Avainsanat: Engineering, Computational Intelligence, Artificial Intelligence (incl. Robotics)