750 search results - page 8 / 150 » Learning SVMs from Sloppily Labeled Data |
ICML
2007
IEEE
14 years 8 months ago
2007
IEEE
In this paper, we survey the current state-ofart models for structured learning problems, including Hidden Markov Model (HMM), Conditional Random Fields (CRF), Averaged Perceptron...
BMCBI
2008
13 years 8 months ago
2008
Background: Predicting a protein's structural or functional class from its amino acid sequence or structure is a fundamental problem in computational biology. Recently, there...
ASUNAM
2010
IEEE
13 years 9 months ago
2010
IEEE
The goal of semi-supervised learning (SSL) methods is to reduce the amount of labeled training data required by learning from both labeled and unlabeled instances. Macskassy and Pr...
SDM
2010
SIAM
13 years 9 months ago
2010
SIAM
This paper addresses the challenging problem of learning from multiple annotators whose labeling accuracy (reliability) differs and varies over time. We propose a framework based ...
KDD
2010
ACM
13 years 11 months ago
2010
ACM
In multi-label learning, each training example is associated with a set of labels and the task is to predict the proper label set for the unseen example. Due to the tremendous (ex...