41,756 research outputs found
Multilevel learning in Kohonen SOM network for classification problems
Classification is one of the most active research and application areas of neural networks. Self-organizing map (SOM) is a feed-forward neural network approach that uses an unsupervised learning algorithm has shown a particular ability for solving the problem of classification in pattern recognition. Classification is the procedure of recognizing classes of patterns that occur in the environment and assigning each pattern to its relevant class. Unlike classical statistical methods, SOM does not require any preventive knowledge about the statistical distribution of the patterns in the environment. In this study, an alternative classification of self organizing neural networks, known as multilevel learning, is proposed to solve the task of pattern separation. The performance of standard SOM and multilevel SOM are evaluated with different distance or dissimilarity measures in retrieving similarity between patterns. The purpose of this analysis is to evaluate the quality of map produced by SOM learning using different distance measures in representing a given dataset. Based on the results obtained from both SOM learning methods, predictions can be made for the unknown samples. This study aims to investigate the performance of standard SOM and multilevel SOM as supervised pattern recognition method. The multilevel SOM resembles the self-organizing map (SOM) but it has several advantages over the standard SOM. Experiments present a comparison between a standard SOM and multilevel SOM for classification of pattern for five different datasets. The results show that the multilevel SOM learning gives good classification rate, however the computational times is increased compared over the standard SOM especially for medium and large scale dataset
Investigation of trends in synoptic patterns over Europe with artificial neural networks
The present study is a comprehensive application of a
methodology developed for the classification of synoptic situations using
artificial neural networks. In this respect, the 500 hPa geopotential height
patterns at 12:00 UTC (Universal Time Coordinated) determined from the
reanalysis data (ERA-40 dataset) of the European Centre for Medium range
Weather Forecasts (ECMWF) over Europe were used. The dataset covers a period
of 45 years (1957–2002) and the neural network methodology applied is the
SOM architecture (Self Organizing Maps). The classification of the synoptic
scale systems was conducted by considering 9, 18, 27 and 36 synoptic
patterns. The statistical analysis of the frequency distribution of the
classification results for the 36 clusters over the entire 44-year period
revealed significant tendencies in the frequency distribution of certain
clusters, thus substantiating a possible climatic change. In the following,
the database was split into two periods, the "reference" period that
includes the first 30 years and the "test" period comprising the remaining
14 years
Application of artificial neural network in market segmentation: A review on recent trends
Despite the significance of Artificial Neural Network (ANN) algorithm to
market segmentation, there is a need of a comprehensive literature review and a
classification system for it towards identification of future trend of market
segmentation research. The present work is the first identifiable academic
literature review of the application of neural network based techniques to
segmentation. Our study has provided an academic database of literature between
the periods of 2000-2010 and proposed a classification scheme for the articles.
One thousands (1000) articles have been identified, and around 100 relevant
selected articles have been subsequently reviewed and classified based on the
major focus of each paper. Findings of this study indicated that the research
area of ANN based applications are receiving most research attention and self
organizing map based applications are second in position to be used in
segmentation. The commonly used models for market segmentation are data mining,
intelligent system etc. Our analysis furnishes a roadmap to guide future
research and aid knowledge accretion and establishment pertaining to the
application of ANN based techniques in market segmentation. Thus the present
work will significantly contribute to both the industry and academic research
in business and marketing as a sustainable valuable knowledge source of market
segmentation with the future trend of ANN application in segmentation.Comment: 24 pages, 7 figures,3 Table
Early Human Vocalization Development: A Collection of Studies Utilizing Automated Analysis of Naturalistic Recordings and Neural Network Modeling
Understanding early human vocalization development is a key part of understanding the origins of human communication. What are the characteristics of early human vocalizations and how do they change over time? What mechanisms underlie these changes? This dissertation is a collection of three papers that take a computational approach to addressing these questions, using neural network simulation and automated analysis of naturalistic data.The first paper uses a self-organizing neural network to automatically derive holistic acoustic features characteristic of prelinguistic vocalizations. A supervised neural network is used to classify vocalizations into human-judged categories and to predict the age of the child vocalizing. The study represents a first step toward taking a data-driven approach to describing infant vocalizations. Its performance in classification represents progress toward developing automated analysis tools for coding infant vocalization types.The second paper is a computational model of early vocal motor learning. It adapts a popular type of neural network, the self-organizing map, in order to control a vocal tract simulator and in order to have learning be dependent on whether the model\u27s actions are reinforced. The model learns both to control production of sound at the larynx (phonation), an early-developing skill that is a prerequisite for speech, and to produce vowels that gravitate toward the vowels in a target language (either English or Korean) for which it is reinforced. The model provides a computationally-specified explanation for how neuromotor representations might be acquired in infancy through the combination of exploration, reinforcement, and self-organized learning.The third paper utilizes automated analysis to uncover patterns of vocal interaction between child and caregiver that unfold over the course of day-long, totally naturalistic recordings. The participants include 16- to 48-month-old children with and without autism. Results are consistent with the idea that there is a social feedback loop wherein children produce speech-related vocalizations, these are preferentially responded to by adults, and this contingency of adult response shapes future child vocalizations. Differences in components of this feedback loop are observed in autism, as well as with different maternal education levels
ARTMAP-FTR: A Neural Network For Fusion Target Recognition, With Application To Sonar Classification
ART (Adaptive Resonance Theory) neural networks for fast, stable learning and prediction have been applied in a variety of areas. Applications include automatic mapping from satellite remote sensing data, machine tool monitoring, medical prediction, digital circuit design, chemical analysis, and robot vision. Supervised ART architectures, called ARTMAP systems, feature internal control mechanisms that create stable recognition categories of optimal size by maximizing code compression while minimizing predictive error in an on-line setting. Special-purpose requirements of various application domains have led to a number of ARTMAP variants, including fuzzy ARTMAP, ART-EMAP, ARTMAP-IC, Gaussian ARTMAP, and distributed ARTMAP. A new ARTMAP variant, called ARTMAP-FTR (fusion target recognition), has been developed for the problem of multi-ping sonar target classification. The development data set, which lists sonar returns from underwater objects, was provided by the Naval Surface Warfare Center (NSWC) Coastal Systems Station (CSS), Dahlgren Division. The ARTMAP-FTR network has proven to be an effective tool for classifying objects from sonar returns. The system also provides a procedure for solving more general sensor fusion problems.Office of Naval Research (N00014-95-I-0409, N00014-95-I-0657
Lifelong Learning of Spatiotemporal Representations with Dual-Memory Recurrent Self-Organization
Artificial autonomous agents and robots interacting in complex environments
are required to continually acquire and fine-tune knowledge over sustained
periods of time. The ability to learn from continuous streams of information is
referred to as lifelong learning and represents a long-standing challenge for
neural network models due to catastrophic forgetting. Computational models of
lifelong learning typically alleviate catastrophic forgetting in experimental
scenarios with given datasets of static images and limited complexity, thereby
differing significantly from the conditions artificial agents are exposed to.
In more natural settings, sequential information may become progressively
available over time and access to previous experience may be restricted. In
this paper, we propose a dual-memory self-organizing architecture for lifelong
learning scenarios. The architecture comprises two growing recurrent networks
with the complementary tasks of learning object instances (episodic memory) and
categories (semantic memory). Both growing networks can expand in response to
novel sensory experience: the episodic memory learns fine-grained
spatiotemporal representations of object instances in an unsupervised fashion
while the semantic memory uses task-relevant signals to regulate structural
plasticity levels and develop more compact representations from episodic
experience. For the consolidation of knowledge in the absence of external
sensory input, the episodic memory periodically replays trajectories of neural
reactivations. We evaluate the proposed model on the CORe50 benchmark dataset
for continuous object recognition, showing that we significantly outperform
current methods of lifelong learning in three different incremental learning
scenario
Classifying Amharic News Text Using Self-Organizing Maps
The paper addresses using artificial neural networks for classification of Amharic news items. Amharic is the language for countrywide communication in Ethiopia and has its own writing system containing extensive systematic redundancy. It is quite dialectally diversified and probably representative of the languages of a continent that so far has received little attention within the language processing field.
The experiments investigated document clustering around user queries using Self-Organizing Maps, an unsupervised learning neural network strategy. The best ANN model showed a precision of 60.0% when trying to cluster unseen data, and a 69.5% precision when trying to classify it
Neural networks for gamma-hadron separation in MAGIC
Neural networks have proved to be versatile and robust for particle
separation in many experiments related to particle astrophysics. We apply these
techniques to separate gamma rays from hadrons for the MAGIC Cerenkov
Telescope. Two types of neural network architectures have been used for the
classi cation task: one is the MultiLayer Perceptron (MLP) based on supervised
learning, and the other is the Self-Organising Tree Algorithm (SOTA), which is
based on unsupervised learning. We propose a new architecture by combining
these two neural networks types to yield better and faster classi cation
results for our classi cation problem.Comment: 6 pages, 4 figures, to be published in the Proceedings of the 6th
International Symposium ''Frontiers of Fundamental and Computational
Physics'' (FFP6), Udine (Italy), Sep. 26-29, 200
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