A Kernel Machine is defined as an alternative to connectionist models, characterized by solid mathematical foundations and efficient learning with generalization capabilities through a regularization framework. AI generated definition based on: Machine Learning (Second Edition), 2024 ...
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...
J. (2008). Kernel methods in machine learning. The Annals of Statistics, 36(3), 1171–1220. https://doi.org/10.1214/009053607000000677. Article MathSciNet MATH Google Scholar Honda, T. (2004). Nonparametric regression with current status data. Annals of the Institute of Statistical ...
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...
An optimization problem in machine learning is usually expressed as the sum of the average of the loss function over training data and a regularization term. Given this type of objective function, it is very computationally expensive to evaluate the full gradient needed in gradient descent, hence ...
getNetDefinition getSampleDataDir getSentiment 内核(kernel) loadImage logisticRegression loss minCount mlModel mutualInformation NeuralNet ngram OneClassSvm optimizer resizeImage rxEnsemble rxFastForest rxFastLinear rxFastTrees rxFeaturize rxHashEnv ...
nite. KERNEL METHODS IN MACHINE LEARNING 5 Definition 3 (Positive de?nite kernel). Let X be a nonempty set. A function k : X × X → R which for all n ∈ N, xi ∈ X , i ∈ [n] gives rise to a positive de?nite Gram matrix is called a positive de?nite kernel. A function...
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...
Multiple kernel learning(MKL)[19] is one of the most popular method to find the optimal linear combination of different kernels, and it has been applied to many machine learning methods, such as SVM[20], fisher discriminant analysis(FDA)[21] and ELM[14,15]. Selecting the Optimal Hidden La...
h x In all of the examples above, despite their differences in definition, nearest neighbor and range queries play important roles in their applications. In probabilistic databases [23], for example...B. Kulis...