Efficient Multi-Class Selective Sampling on Graphs
نویسندگان
چکیده
1.Yang, Peng, and Peilin Zhao. "A Min-Max Optimization Framework For Online Graph Classification." CIKM, 2015. 2.Cesa-Bianchi, Nicolo, Alex Conconi, and Claudio Gentile. "A second-order perceptron algorithm." SIAM Journal on Computing 34.3 (2005): 640-668. We evaluated the performance of baselines and our algorithms with two measurements: cumulative error rate and number of queried labels. The figures below show the performance with respect to online learning rounds, various ratio of queried labels and a sensitive study of low-rank impact on performance.
منابع مشابه
A bijection for plane graphs and its applications
This paper is concerned with the counting and random sampling of plane graphs (simple planar graphs embedded in the plane). Our main result is a bijection between the class of plane graphs with triangular outer face, and a class of oriented binary trees. The number of edges and vertices of the plane graph can be tracked through the bijection. Consequently, we obtain counting formulas and an eff...
متن کاملDesigning Distributed Fixed-Time Consensus Protocols for Linear Multi-Agent Systems Over Directed Graphs
This technical note addresses the distributed fixed-time consensus protocol design problem for multi-agent systems with general linear dynamics over directed communication graphs. By using motion planning approaches, a class of distributed fixed-time consensus algorithms are developed, which rely only on the sampling information at some sampling instants. For linear multi-agent systems, the pro...
متن کاملSelective Sampling Methods in One-Class Classification Problems
Selective sampling, a part of the active learning method, reduces the cost of labeling supplementary training data by asking only for the labels of the most informative, unlabeled examples. This additional information added to an initial, randomly chosen training set is expected to improve the generalization performance of a learning machine. We investigate some methods for a selection of the m...
متن کاملUncertainty sampling methods for one-class classifiers
Selective sampling, a part of the active learning method, reduces the cost of labeling supplementary training data by asking for the labels only of the most informative, unlabeled examples. This additional information added to an initial, randomly chosen training set is expected to improve the generalization performance of a learning machine. We investigate some methods for a selection of the m...
متن کاملBayesian inference for Gaussian graphical models beyond decomposable graphs
Bayesian inference for graphical models has received much attention in the literature in recent years. It is well known that when the graph G is decomposable, Bayesian inference is significantly more tractable than in the general non-decomposable setting. Penalized likelihood inference on the other hand has made tremendous gains in the past few years in terms of scalability and tractability. Ba...
متن کامل