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NeuroCOLT
Technical Reports 1996
NC-TR-96-001:
On digital nondeterminism
Felipe Cucker, Martin Matamala
NC-TR-96-002:
Complexity and Real Computation: A Manifesto
Lenore Blum, Felipe Cucker, Mike Shub, Steve Smale
NC-TR-96-003:
Models for Parallel Computation with Real
Numbers
F. Cucker, J.L. Montana, L.M. Pardo
NC-TR-96-004:
Nash Trees and Nash Complexity
Felipe Cucker, Thomas Lickteig
NC-TR-96-005:
On the computational power and super-Turing
capabilities of dynamical systems
Olivier Bournez, Michel Cosnard
NC-TR-96-006:
Finite Sample Size Results for Robust Model
Selection; Application to
Neural Networks
Joel Ratsaby, Ronny Meir
NC-TR-96-007:
On the structure of $\npoly{C}$
Gregorio Malajovich, Klaus Meer
NC-TR-96-008:
Dynamic Recurrent Neural Networks: a Dynamical
Analysis
Jean-Philippe DRAYE, Davor PAVISIC, Guy CHERON, Gaëtan LIBERT
NC-TR-96-009:
Scale-sensitive Dimensions, Uniform Convergence,
and Learnability
Noga Alon, Shai Ben-David, Nicolò Cesa-Bianchi, David Haussler
NC-TR-96-010:
On-line Prediction and Conversion Strategies
Nicolò Cesa-Bianchi,Yoav Freund, David P. Helmbold, Manfred K. Warmuth
NC-TR-96-011:
Worst-case Quadratic Loss Bounds for Prediction
Using Linear Functions and Gradient Descent
Nicolò Cesa-Bianchi, Philip M. Long, Manfred K. Warmuth
NC-TR-96-012:
Using Bayesian Methods for Avoiding Overfitting
and for Ranking Networks in Multilayer Perceptrons Learning
Michel de Bollivier, Domenico Perrotta
NC-TR-96-013:
Lower Bounds for the Computational Power of
Networks of Spiking Neurons
Wolfgang Maass
NC-TR-96-014:
Analog Computations on Networks of Spiking
Neurons
Wolfgang Maass
NC-TR-96-015:
Vapnik-Chervonenkis Dimension of Neural Nets
Wolfgang Maass
NC-TR-96-016:
On the Computational Power of Noisy Spiking
Neurons
Wolfgang Maass
NC-TR-96-017:
Die Komplexität des Rechnens und Lernens mit
neuronalen Netzen --
Ein Kurzführer
Michael Schmitt
NC-TR-96-018:
Tracking the best disjunction
Peter Auer, Manfred Warmuth
NC-TR-96-019:
Learning Nested Differences in the Presence
of Malicious Noise
Peter Auer
NC-TR-96-020:
Characterizing the Learnability of Kolmogorov
Easy Circuit Expressions
Jose L. Balcazar, Harry Buhrman
NC-TR-96-021:
T2 - Computing optimal 2-level decision tree
Peter Auer
NC-TR-96-022:
Efficient Learning with Virtual Threshold
Gates
Wolfgang Maass, Manfred Warmuth
NC-TR-96-023:
On learnability and predicate logic (Extended
Abstract)
Wolfgang Maass Gy. Turan,
NC-TR-96-024:
Lower Bounds on Identification Criteria for
Perceptron-like Learning Rules
Michael Schmitt
NC-TR-96-025:
On Methods to Keep Learning Away from Intractability
(Extended abstract)
Michael Schmitt
NC-TR-96-026:
Accuracy of techniques for the logical analysis
of data
Martin Anthony
NC-TR-96-027:
Interpolation and Learning in Artificial Neural
Networks
Martin Anthony
NC-TR-96-028:
Threshold Functions, Decision Lists, and the
Representation of Boolean
Functions
Martin Anthony
NC-TR-96-029:
Learning of Depth Two Neural Nets with Constant
Fan-in at the Hidden Nodes
Peter Auer, Stephen Kwek, Wolfgang Maass, Manfred K. Warmuth,
NC-TR-96-030:
Exponentially many local minima for single
neurons
Peter Auer, Mark Herbster, Manfred K. Warmuth
NC-TR-96-031:
An Efficient Implementation of Sigmoidal Neural
Nets in Temporal
Coding with Noisy Spiking Neurons
Wolfgang Maass
NC-TR-96-032:
A Framework for Stuctural Risk Minimisation
John Shawe-Taylor, Peter Bartlett, Robert Williamson, Martin Anthony
NC-TR-96-033:
Learning to Compress Ergodic Sources
by Jonathan Baxter, John Shawe-Taylor
NC-TR-96-034:
Theory and Applications of Agnostic PAC-Learning
with Small Decision Trees
Peter Auer, Robert C. Holte, Wolfgang Maass
NC-TR-96-035:
A recurrent network that performs a context-sensitive
prediction task
Mark Steijvers, Peter Grünwald
NC-TR-96-036:
Tight worst-case loss bounds for predicting
with expert advice
David Haussler, Jyrki Kivinen, Manfred K. Warmuth
NC-TR-96-037:
Exponentiated Gradient Versus Gradient Descent
for Linear Predictors
by Jyrki Kivinen, Manfred K. Warmuth
NC-TR-96-038:
Active Noise Control with Dynamic Recurrent
Neural Networks
Davor Pavisic, Laurent Blondel, Jean-Philipe Draye, Gaëtan Libert,
Pierre Chapelle
NC-TR-96-039:
A Survey on real Structural Complexity Theory
Klaus Meer, Christian Michaux
NC-TR-96-040:
The Computational Power of Spiking Neurons
Depends on the Shape of the Postsynaptic Potentials
Wolfgang Maass, Berthold Ruf
NC-TR-96-041:
Finding Optimal Multi-Splits for Numerical
Attributes in Decision Tree Learning
Tapio Elomaa, Juho Rousu
NC-TR-96-042:
Shattering all Sets of k points in `General
Position' Requires (k-1)/2
Parameters
Eduardo D. Sontag
NC-TR-96-043:
Elimination of Constants from Machines over
Algebraically Closed Fields
Pascal Koiran
NC-TR-96-044:
Hilbert's Nullstellensatz is in the Polynomial
Hierarchy
Pascal Koiran
NC-TR-96-045:
Networks of Spiking Neurons: The Third Generation
of Neural Network Models
Wolfgang Maass
NC-TR-96-046:
Use Of Neural Network Ensembles for Portfolio
Selection and Risk Management
D.L.Toulson, S.P.Toulson
NC-TR-96-047:
A Graph-theoretic Generalization of the Sauer-Shelah
Lemma
Nicolò Cesa-Bianchi, David Haussler
NC-TR-96-048:
A Comparison between Cellular Encoding and
Direct Encoding for Genetic Neural Networks
Frederic Gruau, Darrell Whitley
NC-TR-96-049:
Extended Grzegorczyk Hierarchy in the BSS
Model of Computability
Jean-Sylvestre Gakwaya
NC-TR-96-050:
Learning from Examples and Side Information
Joel Ratsaby, Vitaly Maiorov
NC-TR-96-051:
Complexity and Dimension
Felipe Cucker, Pascal Koiran, Martin Matamala
NC-TR-96-052:
Semi-Algebraic Complexity -- Additive Complexity
of Diagonalization of QuadraticForms
Thomas Lickteig, Klaus Meer
NC-TR-96-053:
Structural Risk Minimization over Data-Dependent
Hierarchies
John Shawe-Taylor, Peter Bartlett, Robert Williamson, Martin Anthony
NC-TR-96-054:
Confidence Estimates of Classification Accuracy
on New Examples
John Shawe-Taylor
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