Michael E. Tipping

发表

Sparse Kernel Principal Component Analysis

Michael E. Tipping, 2000, NIPS.

NeuroScale: Novel Topographic Feature Extraction using RBF Networks

Michael E. Tipping, David Lowe, D. Lowe, 1996, NIPS.

Hierarchical models for data visualization

Christopher M. Bishop, Michael E. Tipping, C. Bishop, 1997 .

A Hierarchical Latent Variable Model for Data Visualization

Christopher M. Bishop, Michael E. Tipping, Charles M. Bishop, 1998, IEEE Trans. Pattern Anal. Mach. Intell..

Latent variable models and data visualisation

Christopher M. Bishop, Michael E. Tipping, C. Bishop, 2000 .

Mixtures of Probabilistic Principal Component Analyzers

Christopher M. Bishop, Michael E. Tipping, Charles M. Bishop, 1999, Neural Computation.

Fast Marginal Likelihood Maximisation for Sparse Bayesian Models

Michael E. Tipping, Anita C. Faul, J. J. Thomson, 2003 .

A variational approach to robust Bayesian interpolation

Neil D. Lawrence, Michael E. Tipping, N. Lawrence, 2003, 2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718).

Advances in Learning Theory: Methods, Models and Applications

Christopher M. Bishop, Michael E. Tipping, C. Bishop, 2003 .

Bayesian Regression and Classification

Christopher M. Bishop, Michael E. Tipping, Charles M. Bishop, 2003 .

Feed-forward neural networks and topographic mappings for exploratory data analysis

Michael E. Tipping, David Lowe, D. Lowe, 1996, Neural Computing & Applications.

Bayesian extension to the language model for ad hoc information retrieval

Djoerd Hiemstra, Hugo Zaragoza, Michael E. Tipping, 2003, SIGIR.

Topographic mappings and feed-forward neural networks

Michael E. Tipping, 1996 .

Sparse Bayesian Learning and the Relevance Vector Machine

Michael E. Tipping, 2001, J. Mach. Learn. Res..

A Kernel Approach for Vector Quantization with Guaranteed Distortion Bounds

Bernhard Schölkopf, Michael E. Tipping, B. Schölkopf, 2001, AISTATS.

Bayesian Extension to the Language Model

Djoerd Hiemstra, Hugo Zaragoza, Michael E. Tipping, 2003, SIGIR 2003.

Use of the Zero-Norm with Linear Models and Kernel Methods

Bernhard Schölkopf, Jason Weston, Michael E. Tipping, 2003, J. Mach. Learn. Res..

Analysis of Sparse Bayesian Learning

Michael E. Tipping, Anita C. Faul, 2001, NIPS.

Variational Relevance Vector Machines

Christopher M. Bishop, Michael E. Tipping, Charles M. Bishop, 2000, UAI.

Bayesian Inference: An Introduction to Principles and Practice in Machine Learning

Michael E. Tipping, 2003, Advanced Lectures on Machine Learning.

Bayesian Image Super-Resolution

Christopher M. Bishop, Michael E. Tipping, Charles M. Bishop, 2002, NIPS.

A novel neural network technique for exploratory data analysis

Michael E. Tipping, David Lowe, 1995 .

Variational inference for Student-t models: Robust Bayesian interpolation and generalised component analysis

Neil D. Lawrence, Michael E. Tipping, Neil D. Lawrence, 2005, Neurocomputing.

Shadow targets: a novel algorithm for topographic projections by radial basis functions

Michael E. Tipping, David Lowe, 1997 .

Probabilistic Visualisation of High-Dimensional Binary Data

Michael E. Tipping, 1998, NIPS.

Shadow targets: A novel algorithm for topographic projections by radial basis functions

Michael E. Tipping, David Lowe, 1998, Neurocomputing.

Personally tailored behavior of computer-controlled virtual figures in a virtual reality environment

Ralf Herbrich, Michael E. Tipping, Mark Hatton, 2004 .

The Relevance Vector Machine

Michael E. Tipping, 1999, NIPS.

A Variational Approach to Robust Regression

Michael E. Tipping, Anita C. Faul, 2001, ICANN.

Understanding forecast reconciliation

Fotios Petropoulos, Michael E. Tipping, Ross Hollyman, 2021, Eur. J. Oper. Res..

Probabilistic Principal Component Analysis

Michael E. Tipping, Charles M. Bishop, 1999 .

Mixtures of Principal Component Analysers

Michael E. Tipping, Charles M. Bishop, 1997 .

Deriving cluster analytic distance functions from Gaussian mixture models

Michael E. Tipping, 1999 .

Generalised Component Analysis

Michael E. Tipping, Neil D. Lawrence, N. Lawrence, 2003 .

Hierarchies Everywhere -- Managing&Measuring Uncertainty in Hierarchical Time Series

Michael E. Tipping, F. Petropoulos, Ross Hollyman, 2022, 2209.15583.

A Variational Approach to Robust Regression

Michael E. Tipping, Anita C. Faul, 2001, International Conference on Artificial Neural Networks.

Probabilistic Principal Component Analysis

Michael E. Tipping, Charles M. Bishop, 1999 .

Probabilistic Principal Component Analysis

Michael E. Tipping, Charles M. Bishop, 1999 .

Probabilistic Principal Component Analysis

Michael E. Tipping, Charles M. Bishop, 1999 .

Mixtures of Probabilistic Principal Component Analyzers

Michael E. Tipping, Charles M. Bishop, 1999, Neural Computation.

Fast Marginal Likelihood Maximisation for Sparse Bayesian Models

Michael E. Tipping, Anita C. Faul, 2003, AISTATS.