55 research outputs found
Automatic knowledge extraction from survey data: Learning M-of-N constructs using a hybrid approach
No full text10.1057/palgrave.jors.2601807Journal of the Operational Research Society5613-14JORS
Constructing auto-associative neural networks: an application to knowledge discovery in a cross-national dy of brand image perception
No full textInternet terminals
No full textThe College of Computer Studies (CCS) made another first in the history of the academe as it set up five INTERNET terminals at the lobby of the Gokongwei Hall. This was in line with DLSU\u27s thrust to be at the forefront of information technology and to expose students to the latest developments in their field of specialization. Dr. Arnulfo Azcarraga was CCS Dean
New appointments beginning AY 2011-2012
No full textDe La Salle University President, Br. Narciso S. Erguiza, Jr. FSC announced the appointment of Dr. Arnulfo P. Azcarraga to the new position of Vice Chancellor for research effective 16 May 2011. This position establishes the separation of the dual positions (Academics and Research) lodged in the Vice Chancellor for Academics and Research. Dr. Myrna S. Austria remains as the Vice Chancellor for Academics. The vacated position of Dr. Azcarraga as EVP for External Linkages and Internationalization will be occupied by Dr. Alvin B. Culaba, effective 16 May 2011
Assessing self-organization using order metrics
No full textProceedings of the International Joint Conference on Neural Networks6159-16485OF
Gender-Specific Classifiers in Phoneme Recognition and Academic Emotion Detection
No full textGender-specific classifiers are shown to outperform general classifiers. In calibrated experiments designed to demonstrate this, two sets of data were used to build male-specific and female-specific classifiers. The first dataset is used to predict vowel phonemes based on speech signals, and the second dataset is used to predict negative emotions based on brainwave (EEG) signals. A Multi-Layered-Perceptron (MLP) is first trained as a general classifier, where all data from both male and female users are combined. This general classifier recognizes vowel phonemes with a baseline accuracy of 91.09%, while that for EEG signals has an average baseline accuracy of 58.70%. The experiments show that the performance significantly improves when the classifiers are trained to be gender-specific–that is, there is a separate classifier for male users, and a separate classifier for female users. For the vowel phoneme recognition dataset, the average accuracy increases to 94.20% and 95.60%, for male only users and female-only users, respectively. As for the EEG dataset, the accuracy increases to 65.33% for male-only users and to 70.50% for female-only users. Performance rates using recall and precision show the same trend. A further probe is done using SOM to visualize the distribution of the sub-clusters among male and female users. © Springer International Publishing AG 2016
Design of a Structured 3D SOM as a Music Archive
No full textA structured 3D SOM is an extension of a Self-Organizing Map from 2D to 3D where a structure has been built into the design of the 3D map. The 3D SOM is a 3x3x3 cube, with a distinct core cube in the center, and 26 exterior cubes around the center. The structured SOM mainly uses the 8 corner cubes among the 26 exterior cubes. Used to build a music archive, the SOM learning algorithm is modified to include a four-step learning and labeling phase. The first phase is meant only to position the music files in their general locations within the core cube. The second phase is meant to position the music files in their respective corner cubes. The third phase is meant to do a fine adjustment of the weight vectors in the core cube. The fourth phase is the labeling of the map and the association of music files to specific nodes in the map. Through the embedded structure of the 3D SOM, a precise measure is developed to measure the quality of the resulting trained SOM (in this case, the music archive), as well as the quality of the different categories/genres of music albums based on a novel measure of the attraction index and the fidelity of music files to their respective music genres. © 2011 Springer-Verlag Berlin Heidelberg
Neural network rule extraction for gaining insight into the characteristics of poverty
No full textNearly one in five families in the country was poor in 2012, according to the Philippine Statistics Authority. While this proportion is lower than the corresponding figures from 2006 and 2009, the absolute number of poor families has actually grown from 3.8 million in 2006 to 4.2 million in 2012 due to the increase in population. Using data samples that have been collected from 69,130 households through a comprehensive community-based monitoring survey conducted in one of the cities that comprise Metro Manila, we attempt to identify the characteristics that differentiate between poor and non-poor households. Using back-propagation neural networks, we are able to correctly predict 73% of the poor households and 60% of the non-poor households. Moreover, the rules extracted from one of these networks provide concise description of how households are classified as poor based on their demographic characteristics and information pertaining to their surrounding living conditions. © 2017, The Natural Computing Applications Forum
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