Alexander J. Smola focuses on Artificial intelligence, Kernel method, Support vector machine, Machine learning and Kernel. His Artificial intelligence research is multidisciplinary, relying on both Margin, Data mining and Pattern recognition. His study looks at the intersection of Kernel method and topics like Reproducing kernel Hilbert space with Applied mathematics and String kernel.
His work on Least squares support vector machine is typically connected to Novelty detection as part of general Support vector machine study, connecting several disciplines of science. In his study, Synchronization and Autoregressive model is inextricably linked to State, which falls within the broad field of Machine learning. His Kernel research incorporates elements of Algorithm, Probability distribution and Kernel.
His primary scientific interests are in Artificial intelligence, Machine learning, Algorithm, Support vector machine and Pattern recognition. His Artificial intelligence research is multidisciplinary, incorporating perspectives in Theoretical computer science and Data mining. His research combines Multi-task learning and Machine learning.
Alexander J. Smola frequently studies issues relating to Function and Algorithm. His study in Support vector machine is interdisciplinary in nature, drawing from both Regularization, Mathematical optimization and Feature vector. His Kernel method study frequently draws connections to other fields, such as Applied mathematics.
His primary areas of investigation include Artificial intelligence, Machine learning, Theoretical computer science, Reinforcement learning and Deep learning. His research integrates issues of Natural language processing and Pattern recognition in his study of Artificial intelligence. His studies deal with areas such as State and Component as well as Machine learning.
The various areas that Alexander J. Smola examines in his Theoretical computer science study include Embedding, Simple and Graph. His Reinforcement learning research includes elements of Control, Sample, Graph and Benchmark. While the research belongs to areas of Automatic summarization, he spends his time largely on the problem of Leverage, intersecting his research to questions surrounding Algorithm.
Alexander J. Smola mostly deals with Artificial intelligence, Machine learning, Theoretical computer science, Deep learning and Graph. His Artificial intelligence research integrates issues from Natural language processing and Pattern recognition. Alexander J. Smola interconnects Python and Raw data in the investigation of issues within Machine learning.
His Theoretical computer science research includes themes of Exponential number, Permutation, Point cloud and Knowledge base. As a part of the same scientific study, Alexander J. Smola usually deals with the Deep learning, concentrating on Noise and frequently concerns with Knowledge graph, Information retrieval and Human voice. His Graph research is multidisciplinary, incorporating elements of Graph, Random walk and Reinforcement learning.
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Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Bernhard Scholkopf;Alexander J. Smola.
Journal of the American Statistical Association (2001)
A tutorial on support vector regression
Alex J. Smola;Bernhard Schölkopf.
Statistics and Computing (2004)
Nonlinear component analysis as a kernel eigenvalue problem
Bernhard Schölkopf;Alexander Smola;Klaus-Robert Müller.
Neural Computation (1998)
Advances in kernel methods: support vector learning
Bernhard Schölkopf;Christopher J. C. Burges;Alexander J. Smola.
international conference on neural information processing (1999)
Estimating the Support of a High-Dimensional Distribution
Bernhard Schölkopf;John C. Platt;John C. Shawe-Taylor;Alex J. Smola.
Neural Computation (2001)
Support Vector Regression Machines
Harris Drucker;Christopher J. C. Burges;Linda Kaufman;Alex J. Smola.
neural information processing systems (1996)
Support Vector Method for Function Approximation, Regression Estimation and Signal Processing
Vladimir Vapnik;Steven E. Golowich;Alex J. Smola.
neural information processing systems (1996)
New Support Vector Algorithms
Bernhard Schölkopf;Alex J. Smola;Robert C. Williamson;Peter L. Bartlett.
Neural Computation (2000)
Kernel Principal Component Analysis
Bernhard Schölkopf;Alexander J. Smola;Klaus-Robert Müller.
international conference on artificial neural networks (1997)
Hierarchical Attention Networks for Document Classification
Zichao Yang;Diyi Yang;Chris Dyer;Xiaodong He.
north american chapter of the association for computational linguistics (2016)
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