85,490 research outputs found
Learning from Noisy Label Distributions
In this paper, we consider a novel machine learning problem, that is,
learning a classifier from noisy label distributions. In this problem, each
instance with a feature vector belongs to at least one group. Then, instead of
the true label of each instance, we observe the label distribution of the
instances associated with a group, where the label distribution is distorted by
an unknown noise. Our goals are to (1) estimate the true label of each
instance, and (2) learn a classifier that predicts the true label of a new
instance. We propose a probabilistic model that considers true label
distributions of groups and parameters that represent the noise as hidden
variables. The model can be learned based on a variational Bayesian method. In
numerical experiments, we show that the proposed model outperforms existing
methods in terms of the estimation of the true labels of instances.Comment: Accepted in ICANN201
A review of domain adaptation without target labels
Domain adaptation has become a prominent problem setting in machine learning
and related fields. This review asks the question: how can a classifier learn
from a source domain and generalize to a target domain? We present a
categorization of approaches, divided into, what we refer to as, sample-based,
feature-based and inference-based methods. Sample-based methods focus on
weighting individual observations during training based on their importance to
the target domain. Feature-based methods revolve around on mapping, projecting
and representing features such that a source classifier performs well on the
target domain and inference-based methods incorporate adaptation into the
parameter estimation procedure, for instance through constraints on the
optimization procedure. Additionally, we review a number of conditions that
allow for formulating bounds on the cross-domain generalization error. Our
categorization highlights recurring ideas and raises questions important to
further research.Comment: 20 pages, 5 figure
- …