منابع مشابه
Logical Vision: One-Shot Meta-Interpretive Learning from Real Images
Statistical machine learning is widely used in image classification. However, most techniques 1) require many images to achieve high accuracy and 2) do not provide support for reasoning below the level of classification, and so are unable to support secondary reasoning, such as the existence and position of light sources and other objects outside the image. In recent work an Inductive Logic Pro...
متن کاملTyped meta-interpretive learning for proof strategies
Formal verification is increasingly used in industry. A popular technique is interactive theorem proving, used for instance by Intel in HOL light. The ability to learn and re-apply proof strategies from a small set of proofs would significantly increase the productivity of these systems, and make them more cost-effective to use. Previous attempts have had limited success, which we believe is a ...
متن کاملMeta-Interpretive Learning of Data Transformation Programs
Data transformation involves the manual construction of large numbers of special-purpose programs. Although typically small, such programs can be complex, involving problem decomposition, recursion, and recognition of context. Building such programs is common in commercial and academic data analytic projects and can be labour intensive and expensive, making it a suitable candidate for machine l...
متن کاملLogical Minimisation of Meta-Rules Within Meta-Interpretive Learning
Meta-Interpretive Learning (MIL) is an ILP technique which uses higher-order meta-rules to support predicate invention and learning of recursive definitions. In MIL the selection of meta-rules is analogous to the choice of refinement operators in a refinement graph search. The meta-rules determine the structure of permissible rules which in turn defines the hypothesis space. On the other hand, ...
متن کاملLearning to Label Aerial Images from Noisy Data
When training a system to label images, the amount of labeled training data tends to be a limiting factor. We consider the task of learning to label aerial images from existing maps. These provide abundant labels, but the labels are often incomplete and sometimes poorly registered. We propose two robust loss functions for dealing with these kinds of label noise and use the loss functions to tra...
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: Machine Learning
سال: 2018
ISSN: 0885-6125,1573-0565
DOI: 10.1007/s10994-018-5710-8