Titlebook: Machine Learning and Knowledge Discovery in Databases; European Conference, Hendrik Blockeel,Kristian Kersting,Filip Železný Conference pro

Glutinous

justify

Tractable Semi-supervised Learning of Complex Structured Prediction Modelsallow the direct use of tractable inference/learning algorithms (e.g., binary label or linear chain). Therefore, these methods cannot be applied to problems with complex structure. In this paper, we propose an approximate semi-supervised learning method that uses piecewise training for estimating th

咆哮

PSSDL: Probabilistic Semi-supervised Dictionary Learninglability of the large labeled datasets. However, in many real world applications, accessing to sufficient labeled data may be expensive and/or time consuming, but its relatively easy to acquire a large amount of unlabeled data. In this paper, we propose a probabilistic framework for discriminative d

小虫

Embedding with Autoencoder Regularizationan guarantee the “semantics” of the original high-dimensional data. Most of the existing embedding algorithms perform to maintain the . property. In this study, inspired by the remarkable success of representation learning and deep learning, we propose a framework of embedding with autoencoder regul

Gum-Disease

RODE

FLAIL

Locally Linear Landmarks for Large-Scale Manifold Learninga graph Laplacian. With large datasets, the eigendecomposition is too expensive, and is usually approximated by solving for a smaller graph defined on a subset of the points (landmarks) and then applying the Nyström formula to estimate the eigenvectors over all points. This has the problem that the

Loathe

松紧带

教唆

Parallel Boosting with Momentumes of the accelerated gradient method while taking into account the curvature of the objective function. We describe a . implementation of BOOM which is suitable for massive high dimensional datasets. We show experimentally that BOOM is especially effective in large scale learning problems with rare yet informative features.