getNetDefinition getSampleDataDir getSentiment 内核(kernel) loadImage logisticRegression loss minCount mlModel mutualInformation NeuralNet ngram OneClassSvm optimizer resizeImage rxEnsemble rxFastForest rxFastLinear rxFastTrees rxFeaturize rxHashEnv ...
Fig. 2: The model families in quantum machine learning. a While data re-uploading models are by definition a generalization of linear quantum models, our exact mappings demonstrate that any polynomial-size data re-uploading model can be realized by a polynomial-size explicit linear model. b Ker...
Graph kernels, a relatively new approach to graph comparison, have been proposed as a theoretically sound approach to the problem of graph comparison and one appearing to hold much promise. Indeed, upon definition of a graph kernel, an entire family of data mining and machine learning algorithms ...
. definition 2 an online learning algorithm is said to achieve the \(\varepsilon\) -approximate \(\mathcal {o}\left( \frac{\log \,t}{t}\right)\) rate if the gap between the objective values of the current and optimal models decreases with the rate \(\mathcal {o}\left( \frac{...
Multiple Kernel Learning (MKL) is a machine learning approach that allows for the integration of multiple features, such as genes, proteins, and metabolites, by combining them as different kernel matrices. These matrices are then used as input for various inference tasks, such as classification and...
Definition Chapters and Articles Related Terms Recommended Publications Featured Authors Chapters and Articles You might find these chapters and articles relevant to this topic. Chapter Learning in Reproducing Kernel Hilbert Spaces Machine Learning Book2015, Machine Learning Sergios Theodoridis Explore book 11....
JanusGraph open-source, distributed graph database 待更新 https://github.com/JanusGraph/janusgraph OpenMLDB an open-source machine learning database 待更新 https://github.com/4paradigm/OpenMLDBtaobao MySQL 数据库内核月报 :http://mysql.taobao.org/monthly/17...
A related important step is the definition and study of the relevance of the measure of maximal variation which can be used to obtain an efficient way for detecting structure in the data and handling missing values.The text is glued together to a consistent story by the addition of new ...
NIMG-74. A NOVEL APPROACH FOR PERSONALIZED RADIOTHERAPY TARGET VOLUME DEFINITION USING METABOLIC MRI AND DEEP LEARNING-DRIVEN PREDICTIONS OF GLIOBLASTOMA R... BACKGROUND. Machine-learning models have demonstrated great promise in predicting tumor infiltration beyond anatomical margins in glioblastoma. Yet ...
Kernel methods in Quantum Machine Learning (QML) have recently gained significant attention as a potential candidate for achieving a quantum advantage in data analysis. Among other attractive properties, when training a kernel-based model one is guarante