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

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书目名称Machine Learning and Knowledge Discovery in Databases网络公开度学科排名




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书目名称Machine Learning and Knowledge Discovery in Databases被引频次学科排名




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书目名称Machine Learning and Knowledge Discovery in Databases年度引用学科排名




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书目名称Machine Learning and Knowledge Discovery in Databases读者反馈学科排名

Lyme-disease

Parallel Boosting with Momentumorithm, which we call BOOM, for .sting with .omentum, enjoys the merits of both techniques. Namely, BOOM retains the momentum and convergence properties of the accelerated gradient method while taking into account the curvature of the objective function. We describe a . implementation of BOOM which

乐意

Inner Ensembles: Using Ensemble Methods Inside the Learning Algorithmied in many more situations than they have been previously. Instead of using them only to combine the output of an algorithm, we can apply them to the decisions made inside the learning algorithm, itself. We call this approach Inner Ensembles. The main contribution of this work is to demonstrate how

EXTOL

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yohimbine

Bundle CDN: A Highly Parallelized Approach for Large-Scale ℓ1-Regularized Logistic Regression by their divergence under high degree of parallelism (DOP), or need data pre-process to avoid divergence. To better exploit parallelism, we propose a coordinate descent based parallel algorithm without needing of data pre-process, termed as Bundle Coordinate Descent Newton (BCDN), and apply it to l

Foolproof

MORD: Multi-class Classifier for Ordinal Regression only allows to design new learning algorithms for ordinal regression using existing methods for multi-class classification but it also allows to derive new models for ordinal regression. For example, one can convert learning of ordinal classifier with (almost) arbitrary loss function to a convex un

tic-douloureux

Identifiability of Model Properties in Over-Parameterized Model Classess (.,.(.))), and the space of queries for the learned model (predicting function values for new examples .). However, in many learning scenarios the 3-way association between hypotheses, data, and queries can really be much looser. Model classes can be over-parameterized, i.e., different hypotheses

祝贺

Exploratory Learninged examples are provided for some classes. In this paper we present variants of well-known semi-supervised multiclass learning methods that are robust when the data contains an unknown number of classes. In particular, we present an “exploratory” extension of expectation-maximization (EM) that explo

Myocyte

Semi-supervised Gaussian Process Ordinal Regressionn while unlabeled ordinal data are available in abundance. Designing a probabilistic semi-supervised classifier to perform ordinal regression is challenging. In this work, we propose a novel approach for semi-supervised ordinal regression using Gaussian Processes (GP). It uses the expectation-propag