Graph-based semi-supervised learning (SSL) methods play an increasingly important role in practical machine learning systems, particularly in agnostic settings when no parametric information or other prior knowledge is available about the data distribution. Given the constructed graph represented by a weight matrix, transductive inference is used to propagate known labels to predict the values ...

Abstract Multi-label learning is a machine subclass that aims to assign more than one label simultaneously for each instance. Many real-world tasks include high-dimensional data which reduces the performance of methods. To solve this issue, filter and multi-label feature selection proposed in paper. The main idea method choose highly relevant non-redundant features with lowest information loss....

eNCE (edge label neighborhood controlled) graph grammars belong to the most powerful graph rewriting systems with single-node graphs on the left-hand side of the productions. From an algorithmic point of view, connuent and boundary eNCE graph grammars are the most interesting subclasses of eNCE graph grammars. In connuent eNCE graph grammars, the order in which nonterminal nodes are substituted...

We present a framework for object segmentation using depth maps based on Random Forest and Graph-cuts theory, and apply it to the segmentation of human limbs. First, from a set of random depth features, Random Forest is used to infer a set of label probabilities for each data sample. This vector of probabilities is used as unary term in α − β swap Graph-cuts algorithm. Moreover, depth values of...

Given a digraph (directed graph) with a labeling on its arcs, we study the problem of finding the Eulerian circuit of lexicographically minimum label. We prove that this problem is NP-complete in general, but if the labelling is locally injective (arcs going out from each vertex have different labels), we prove that it is solvable in linear time by giving an algorithm that constructs this circu...

A novel reranking method has been developed to refine web search queries. A label propagation algorithm was applied on a clickthrough graph, and the candidates were reranked using a query language model. Our method first enumerates query candidates with common landing pages with regard to the given query to create a clickthrough graph. Second, it calculates the likelihood of the candidates, usi...

Let \(G\) be a quasi-transitive, locally finite, connected graph rooted at vertex \(o\), and let \(c_n(o)\) the number of self-avoiding walks length \(n\) on starting \(o\). We show that if has only thin ends, then generating function \(F_{\mathrm{SAW},o}(z)=\sum_{n \geq 1} c_n(o) z^n\) is an algebraic function. In particular, connective constant such number.If deterministically edge-labelled, ...

We address the problem of semi-supervised learning in relational networks, networks in which nodes are entities and links are the relationships or interactions between them. Typically this problem is confounded with the problem of graph-based semi-supervised learning (GSSL), because both problems represent the data as a graph and predict the missing class labels of nodes. However, not all graph...

Recognizing speech act types in Twitter is of much theoretical interest and practical use. Our previous research did not adequately address the deficiency of training data for this multi-class learning task. In this work, we set out by assuming only a small seed training set and experiment with two semi-supervised learning schemes, transductive SVM and graph-based label propagation, which can l...

Logical structure analysis of document images is an important problem in document image understanding. In this paper, we propose a graph matching approach to label logical components on a document page. Our system is able to learn a model for a document class, use this model to label document images through graph matching, and adaptively improve the model with error feed back. We tested our met...