Unsupervised Learning for Graph Matching
نویسندگان
چکیده
منابع مشابه
Unsupervised learning of graph languages using kernels
In most domains, machine learning involves discovering the relationship between objects. Such relationships are naturally represented as graphs. Up until now, most work on learning graphs consisted of extending well-known approaches from the field of machine learning to deal with more complex structures. For both technical and conceptual reasons, borrowing techniques from Grammar Induction (GI)...
متن کاملUnsupervised Learning of Morphology with Graph Sampling
We introduce a language-independent, graph-based probabilistic model of morphology, which uses transformation rules operating on whole words instead of the traditional morphological segmentation. The morphological analysis of a set of words is expressed through a graph having words as vertices and structural relationships between words as edges. We define a probability distribution over such gr...
متن کاملRecognizing Indoor Images with Unsupervised Segmentation and Graph Matching
In this paper we address the problem of recognizing scenes by performing unsupervised segmentation followed by matching the resulting adjacency region graph. Our segmentation method is an adaptive extension of the Asymetric Clustering Model, a distributional clustering method based on the EM algorithm, whereas our matching proposal consists of embodying the Graduated Assignement cost function i...
متن کاملUnsupervised learning of edit parameters for matching name variants
Since named entities are often written in different ways, question answering (QA) and other language processing tasks stand to benefit from entity matching. We address the problem of finding equivalent person names in unstructured text. Our approach is a generalization of spelling correction: We compare to candidate matches by applying a set of edits to an input name. We introduce a novel unsup...
متن کاملScalable Methods for Graph-Based Unsupervised and Semi-Supervised Learning
Data often comes in the form of a graph. When it does not, it often makes sense to represent it as a graph for learning tasks that rely on the similarities or relationships between data points. As data size grows, traditional methods for learning on graphs often become computationally intractable in terms of time and space requirements. We describe new methods for graph-based clustering and sem...
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: International Journal of Computer Vision
سال: 2011
ISSN: 0920-5691,1573-1405
DOI: 10.1007/s11263-011-0442-2