Thursday, July 31, 2014

 

 



Soy un nuevo usuario

Olvidé mi contraseña

Entrada usuarios

Lógica Matemáticas Astronomía y Astrofísica Física Química Ciencias de la Vida
Ciencias de la Tierra y Espacio Ciencias Agrarias Ciencias Médicas Ciencias Tecnológicas Antropología Demografía
Ciencias Económicas Geografía Historia Ciencias Jurídicas y Derecho Lingüística Pedagogía
Ciencia Política Psicología Artes y Letras Sociología Ética Filosofía


Comparison of a Bayesian SOM with the EM algorithm for Gaussian mixtures

1) La descarga del recurso depende de la página de origen
2) Para poder descargar el recurso, es necesario ser usuario
    registrado en Universia

  Descargar recurso

Detalles del recurso

Pertenece a: Faculty of Technology ePrints Service  

Descripción: A Bayesian SOM (BSOM) [8], is proposed and applied to the unsupervised learning of Gaussian mixture distributions and its performance is compared with the expectation-maximisation (EM) algorithm. The BSOM is found to yield as good results as the well-known EM algorithm but with much fewer iterations and, more importantly it can be used as an on-line training method. The neighbourhood function and distance measures of the traditional SOM [3] are replaced by the neuron's on-line estimated posterior probabilities, which can be interpreted as a Bayesian inference of the neuron's opportunity to share in the winning response and so to adapt to the input pattern. Such posteriors starting from uniform priors are gradually sharpened when more and more data samples become available and so improve the estimation of model parameters. Each neuron then converges to one component of the mixture. Experimental results are compared with those of the EM algorithm.

Autor(es): Yin, Hujun -  Allinson, Nigel - 

Id.: 55198554

Versión: 1.0

Estado: Final

Tipo:  application/pdf - 

Palabras claveG400 Computer Science - 

Tipo de recurso: Conference or Workshop Item  -  PeerReviewed  - 

Tipo de Interactividad: Expositivo

Nivel de Interactividad: muy bajo

Audiencia: Estudiante  -  Profesor  -  Autor  - 

Estructura: Atomic

Coste: no

Copyright: sí

Formatos:  application/pdf - 

Requerimientos técnicos:  Browser: Any - 

Relación: [References] http://users.ics.tkk.fi/wsom97/program.html
[References] http://eprints.lincoln.ac.uk/5020/

Fecha de contribución: 13-oct-2012

Contacto:

Localización:
* Yin, Hujun and Allinson, Nigel (1997) Comparison of a Bayesian SOM with the EM algorithm for Gaussian mixtures. In: Workshop on Self-Organising Maps (WSOM'97), 4-6 June 1997, Helsinki, Finland.


Otros recursos del mismo autor(es)

  1. Building recognition in urban environments: A survey of state-of-the-art and future challenges Building recognition in urban environments aims to identify different buildings in a large-scale ima...
  2. Ionizing and non Ionizing radiation damage in a large area CMOS active pixel sensor for medical applications Currently, large-area medical sensors are based on amorphous flat panel technology. Sensors based on...
  3. PRaVDA: seeing and treating cancer with protons Annually over 300, 000 people are diagnosed with cancer in the UK. Radiotherapy accounts for 40% of ...
  4. Performance of a novel wafer scale CMOS active pixel sensor for bio-medical imaging Recently CMOS Active Pixels Sensors (APSs) have become a valuable alternative to amorphous Silicon a...
  5. Proton-counting radiography for proton therapy: a proof of principle using CMOS APS technology Despite the early recognition of the potential of proton imaging to assist proton therapy (Cormack 1...

Otros recursos de la misma colección

  1. Investigating the understanding, use and experiences of older people in Lincolnshire accessing 999 and NHS 111: A scoping study Background Thousands of 999 calls are made to ambulance services in England that could be resolved m...
  2. What do users value about the emergency ambulance service? Background Response times are currently the dominant indicator for measuring the quality of emergenc...
  3. Open access: effective measures to put UK research online under threat? The universities of the UK should not squander the opportunity to put in place an effective mechanis...
  4. Systematic review: barriers and facilitators for minority ethnic groups accessing urgent and prehospital care Background Research addressing inequalities has focussed predominantly on primary and acute care. We...
  5. Social bookmarking pedagogies in higher education: a comparative study This paper compares two projects that adopted social bookmarking (SB) technology in different educat...

Valoración de los usuarios

No hay ninguna valoración para este recurso.Sea el primero en valorar este recurso.
 

Busque un recurso