Semi-Supervised Learning with Uncertainty
نویسندگان
چکیده
منابع مشابه
Semi-Supervised Learning with Trees
We describe a nonparametric Bayesian approach to generalizing from few labeled examples, guided by a larger set of unlabeled objects and the assumption of a latent tree-structure to the domain. The tree (or a distribution over trees) may be inferred using the unlabeled data. A prior over concepts generated by a mutation process on the inferred tree(s) allows efficient computation of the optimal...
متن کاملSemi-Supervised Learning with Heterophily
We propose a novel linear semi-supervised learning formulation that is derived from a solid probabilistic framework: belief propagation. We show that our formulation generalizes a number of label propagation algorithms described in the literature by allowing them to propagate generalized assumptions about influences between classes of neighboring nodes. We call this formulation Semi-Supervised ...
متن کاملCoupled Semi-Supervised Learning
This thesis argues that successful semi-supervised learning is improved by learning many functions at once in a coupled manner. Given knowledge about constraints between functions to be learned (e.g., f1(x) → ¬f2(x)), forcing the models that are learned to obey these constraints can yield a more constrained, and therefore easier, set of learning problems. We apply these ideas to bootstrap learn...
متن کاملSemi-supervised Learning
Semi-supervised learning uses both labeled and unlabeled data to perform an otherwise supervised learning or unsupervised learning task. In the former case, there is a distinction between inductive semi-supervised learning and transductive learning. In inductive semi-supervised learning, the learner has both labeled training data {(xi, yi)}i=1 iid ∼ p(x, y) and unlabeled training data {xi} i=l+...
متن کاملSemi-Supervised Learning
For many classification problems, unlabeled training data are inexpensive and readily available, whereas labeling training data imposes costs. Semi-supervised classification algorithms aim at utilizing information contained in unlabeled data in addition to the (few) labeled data. Semi-supervised (for an example, see Seeger, 2001) has a long tradition in statistics (Cooper & Freeman, 1970); much...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Transactions of the Japanese Society for Artificial Intelligence
سال: 2018
ISSN: 1346-0714,1346-8030
DOI: 10.1527/tjsai.c-ha2