Press "Enter" to skip to content

Adaptive and Natural Computing Algorithms: 10th by Dominik Olszewski (auth.), Andrej Dobnikar, Uroš Lotrič,

By Dominik Olszewski (auth.), Andrej Dobnikar, Uroš Lotrič, Branko à ter (eds.)

The two-volume set LNCS 6593 and 6594 constitutes the refereed complaints of the tenth foreign convention on Adaptive and average Computing Algorithms, ICANNGA 2010, held in Ljubljana, Slovenia, in April 2010. The eighty three revised complete papers awarded have been rigorously reviewed and chosen from a complete of one hundred forty four submissions. the second one quantity contains forty-one papers geared up in topical sections on trend attractiveness and studying, tender computing, structures conception, help vector machines, and bioinformatics.

Show description

Read Online or Download Adaptive and Natural Computing Algorithms: 10th International Conference, ICANNGA 2011, Ljubljana, Slovenia, April 14-16, 2011, Proceedings, Part II PDF

Best computing books

MySQL Troubleshooting: What To Do When Queries Don't Work

Caught with insects, functionality difficulties, crashes, facts corruption, and complicated output? If you’re a database programmer or DBA, they’re a part of your lifestyles. The trick is understanding the way to fast get over them. This specified, example-packed ebook exhibits you the way to deal with an array of vexing difficulties whilst operating with MySQL.

Raspberry Pi Cookbook

The area of Raspberry Pi is evolving quick, with many new interface forums and software program libraries changing into to be had forever. during this cookbook, prolific hacker and writer Simon Monk offers greater than 2 hundred sensible recipes for operating this tiny inexpensive machine with Linux, programming it with Python, and hooking up sensors, automobiles, and different hardware—including Arduino.

Adaptive and Natural Computing Algorithms: 10th International Conference, ICANNGA 2011, Ljubljana, Slovenia, April 14-16, 2011, Proceedings, Part II

The two-volume set LNCS 6593 and 6594 constitutes the refereed complaints of the tenth foreign convention on Adaptive and common Computing Algorithms, ICANNGA 2010, held in Ljubljana, Slovenia, in April 2010. The eighty three revised complete papers offered have been rigorously reviewed and chosen from a complete of a hundred and forty four submissions.

Soft Computing for Image and Multimedia Data Processing

Right research of photo and multimedia facts calls for effective extraction and segmentation ideas. one of the computational intelligence ways, the tender computing paradigm is better built with a number of instruments and methods that comprise clever thoughts and ideas. This booklet is devoted to item extraction, photo segmentation, and side detection utilizing smooth computing options with wide real-life software to snapshot and multimedia facts.

Additional info for Adaptive and Natural Computing Algorithms: 10th International Conference, ICANNGA 2011, Ljubljana, Slovenia, April 14-16, 2011, Proceedings, Part II

Example text

The EM GMM and SOMkM are faster than GSOM for approximately 10 times. Except for the data sets Ring and LetterABC, GSOM correctly finds the expected number of clusters. 4 Conclusion A novel approach of clustering Kohonen’s SOM is presented in the paper, utilizing gravitational clustering in a two-level scheme. According to the results of the experiments, the advantages of the presented method GSOM are as follows. First, GSOM is able to detect and to successfully cluster data of complex shapes with linearly non-separable regions.

KNN1 does not perform well, but the features have a strong influence on its predictions. We can conclude it overfits. Fig. 5. M5P successfully models dDisj and correctly predicts R = 1. The visualization shows that a single feature is responsible for the prediction, while the other two have the opposite effect. Fig. 6. The neural network successfully models dXorBin and correctly predicts this instance. The explanation reveals that the first three features are important and all three contribute towards 1.

Data Mining: Introductory and Advanced Topics. Prentice Hall, Englewood Cliffs (2003) 2. : Data mining: practical machine learning tools and techniques. Elsevier, Amsterdam (2005) 3. : Self-organizing maps. Springer, Heidelberg (2001) 4. : Clustering of the Self-Organizing Map. IEEE Trans. on Neural Networks 11(3), 586–600 (2000) 5. : Emergence in Self Organizing Feature Maps. In: 6th International Workshop on Self-Organizing Maps (2007) 6. : Automatic Cluster Detection in Kohonen’s SOM. IEEE Trans.

Download PDF sample

Rated 4.30 of 5 – based on 20 votes