Tino, Peter - School of Computer Science, University of Birmingham

• Measuring Generalization Performance in Co-evolutionary Siang Y. Chong, Peter Ti~no, and Xin Yao
• A brief survey of linear http://cogs.csustan.edu/~ tom/linear-algebra
• (Introduction to) Neural Computation Bias vs. Variance
• Intelligent Data Analysis Topographic Maps of Vectorial Data
• Non-linear Prediction of Quantitative StructureActivity Relationships
• The University of Birmingham School of Computer Science
• S.V.N. "Vishy" Vishwanathan: Basic Mathematics, Page 1 Basic Mathematics
• in Advances in Neural Information Processing Systems 12 S.A. Solla, T.K. Leen and K.R. Muller (eds.), 52--58
• VISUALISATION OF INCOMPLETE DATA USING CLASS INFORMATION CONSTRAINTS
• Astronomy & Astrophysics manuscript no. 4652 May 1, 2006 (DOI: will be inserted by hand later)
• Introduction to the School of Computer Science
• Bifurcation Structure of Equilibria of Iterated Softmax School of Computer Science
• Markovian Architectural Bias of Recurrent Neural Peter Tino Michal Cernansky Lubica Benuskova
• Intelligent Data Analysis Demonstration of PCA and SOM
• Finite State Machines and Recurrent Neural Networks --Automata and Dynamical Systems Approaches
• TO APPEAR IN IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS, PART B: CYBERNETICS, 1999. 1 Spatial Representation of Symbolic Sequences
• Learning long--term dependencies in NARX recurrent neural networks \Lambda
• Architectural Bias in Recurrent Neural Networks Fractal Analysis
• Engineering Notes Ice Shape Characterization Using
• Attractive Periodic Sets in Discrete Time Recurrent Networks (with Emphasis on Fixed Point Stability and
• On Non-Markovian Topographic Organization of Receptive Fields in Recursive Self-Organizing
• Breiman, L. (1996). Bias, variance, and arcing classifiers. Unpublished manuscript. Buntine, W. , and Weigend, A. (1991). ``Bayesian backpropagation''. Complex Systems, 5, 603
• Associative Neural Networks 3.1 Linear Associator Neural Network
• A Symbolic Dynamics Approach to Volatility Prediction Peter Tino \Lambda Christian Schittenkopf Georg Dorffner y
• 0 0.2 0.4 0.6 0.8 1 CBR -coding DNA
• At Birmingham, we encourage our students to get the most out of their time at University. At the end of your studies, you
• The profitability of trading volatility using realvalued and symbolic models
• Volatility Trading via Temporal Pattern Recognition in Quantized Financial Time Series
• 716 IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS--PART B: CYBERNETICS, VOL. 29, NO. 6, DECEMBER 1999 Simultaneous Training of Negatively
• Doctoral Thesis Efficient Computation in Networks of
• Semi-Supervised Learning of Hierarchical Latent Trait Models for Data Visualisation
• Neural Networks -Basis P.Tino@cs.bham.ac.uk
• An Introduction to Statistical Machine Learning
• A Principled Approach to Interactive Hierarchical NonLinear Visualization of
• Recurrent neural networks with small weights implement definite memory machines
• SWW1 -Week 1 School of Computer Science
• UNIVERSITY OF BIRMINGHAM School of Computer Science
• IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. X, NO. X, XX 200X 1 Predictive Ensemble Pruning by Expectation
• Learning Beyond Finite Memory in Recurrent Networks Of Spiking Neurons
• Using Directional Curvatures to Visualize Folding Patterns of the GTM Projection
• Financial Volatility Trading using Recurrent Neural Networks
• Hierarchical GTM: constructing localized non-linear projection manifolds in a
• Extracting Finite State Representations from Recurrent Neural Networks trained on Chaotic Symbolic Sequences
• Searching for co-expressed genes in three-color cDNA microarray data using a probabilistic model
• Learning Beyond Finite Memory in Recurrent Networks Of Spiking Neurons
• (Introduction to) Neural Computation School of Computer Science
• IEEE TRANSACTIONS ON NEURAL NETWORKS 1 Visualisation of Tree-Structured Data through
• UNIVERSITY OF BIRMINGHAM School of Computer Science
• Welcome to the MOMD Fair Need help registering for If so just follow these step by step instructions...
• Making sense of sparse rating data in collaborative filtering via topographic organization of user preference
• in Advances in Neural Information Processing Systems 12 S.A. Solla, T.K. Leen and K.R. Muller (eds.), 645--651
• Multifractal properties of Hao's geometric representations of DNA sequences
• SemiSupervised Construction of General Visualization Hierarchies
• A General Framework for a Principled Hierarchical Visualization of Multivariate Data
• Learning and Extracting Initial Mealy Automata With a Modular Neural Network Model
• Extracting Stochastic Machines from Recurrent Neural Networks Trained on Complex Symbolic Sequences
• Understanding State Space Organization in Recurrent Neural Networks with Iterative
• Markovian bias of neural-based architectures with feedback connections
• Year 1 Modules Outside the Main Discipline Dr Peter Tino
• Semi-Supervised Learning of Hierarchical Latent Trait Models for Data Visualisation
• Does Money Matter in Inflation Forecasting? JM Binner 1
• arXiv:0908.3706v1[astro-ph.CO]25Aug2009 Uncovering delayed patterns in noisy and
• Generalization and Diversity in Co-evolutionary Learning Siang Yew Chong, Peter Tino, and Xin Yao
• Probabilistic Classification Vector Machines Huanhuan Chen, Student Member, IEEE, Peter Tino, and Xin Yao, Fellow, IEEE
• Journal of Machine Learning Research 6 (2005) 16211650 Submitted 4/04; Published 9/05 Managing Diversity in Regression Ensembles
• Adapting to NAT timeout values in P2P Overlay Richard Price and Peter Tino
• Metric Properties of Structured Data Visualizations through Generative Probabilistic Modeling
• A kernel-based approach to estimating phase shifts between irregularly sampled time series
• Recursive Self-Organizing Map as a Contractive Iterative Function System
• Volatility Forecasting with Sparse Bayesian Kernel Peter Tino1
• Evaluation of Adaptive Nature Inspired Task Allocation Against Alternate Decentralised
• INTRODUCING A STAR TOPOLOGY INTO LATENT CLASS MODELS FOR
• A Generative Probabilistic Approach to Visualizing Sets of Symbolic Sequences
• Dynamics and Topographic Organization of Recursive Self-Organizing Maps
• AFRICAN STUDIES 5 ARABIC LANGUAGE 10
• Peter Tino -School of Computer Science P.Tino@cs.bham.ac.uk
• Intelligent Data Analysis Principal Component Analysis
• (Introduction to) Neural Computation Gradient Learning
• Self Organizing Maps (SOMs) Introduction to Neural Computation : Guest Lecture 1
• Learning Vector Quantization (LVQ) Introduction to Neural Computation : Guest Lecture 2
• 2001 Special issue From articial neural networks to spiking neuron populations
• Optimization of the Backpropagation Algorithm for Training Multilayer Perceptrons
• Contributed article Ensemble learning via negative correlation
• An Alternative Model for Mixtures of Dept. of Computer Science, The Chinese University of Hong Kong
• Analysing a Neural Network trained by Backpropagation Andrew J Brown Report Introduction to Neural Computation School of Computer Science
• NEURAL NETWORKS Ivan F Wilde
• Week 2 -Algorithms School of Computer Science
• IEEE TRANSACTIONS ON NEURAL NETWORKS 1 Minimum Complexity Echo State Network
• Simple Deterministically Constructed Cycle Reservoirs with Regular Jumps
• Degree distribution and scaling in the connecting -nearest -neighbors model