Sciweavers

5 search results - page 1 / 1
» Fast Generation of a Sequence of Trained and Validated Feed-...
Sort
View
FLAIRS
2006
13 years 6 months ago
Fast Generation of a Sequence of Trained and Validated Feed-Forward Networks
In this paper, three approaches are presented for generating and validating sequences of different size neural nets. First, a growing method is given along with several weight ini...
Pramod Lakshmi Narasimha, Walter Delashmit, Michae...
BMCBI
2007
130views more  BMCBI 2007»
13 years 4 months ago
HMM-ModE - Improved classification using profile hidden Markov models by optimising the discrimination threshold and modifying e
Background: Profile Hidden Markov Models (HMM) are statistical representations of protein families derived from patterns of sequence conservation in multiple alignments and have b...
Prashant K. Srivastava, Dhwani K. Desai, Soumyadee...
ICDAR
2003
IEEE
13 years 10 months ago
Generation of Hierarchical Dictionary for Stroke-order Free Kanji Handwriting Recognition Based on Substroke HMM
This paper describes a method of generating a Kanji hierarchical structured dictionary for stroke-number and stroke-order free handwriting recognition based on substroke HMM. In s...
Mitsuru Nakai, Hiroshi Shimodaira, Shigeki Sagayam...
BMCBI
2007
141views more  BMCBI 2007»
13 years 4 months ago
Artificial neural network models for prediction of intestinal permeability of oligopeptides
Background: Oral delivery is a highly desirable property for candidate drugs under development. Computational modeling could provide a quick and inexpensive way to assess the inte...
Eunkyoung Jung, Junhyoung Kim, Minkyoung Kim, Dong...
BMCBI
2010
227views more  BMCBI 2010»
13 years 4 months ago
Accurate and efficient gp120 V3 loop structure based models for the determination of HIV-1 co-receptor usage
Background: HIV-1 targets human cells expressing both the CD4 receptor, which binds the viral envelope glycoprotein gp120, as well as either the CCR5 (R5) or CXCR4 (X4) co-recepto...
Majid Masso, Iosif I. Vaisman