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NeuroCOLT
Technical Reports 1995
NC-TR-95-001:
Worst-Case Analysis of the Bandit Problem
Peter Auer, Nicolò Cesa-Bianchi
NC-TR-95-002:
Agnostic PAC-Learning of Functions on Analog
Neural Nets
Wolfgang Maass
NC-TR-95-003:
Perspectives of Current Research about the
Complexity of Learning on
Neural Nets
Wolfgang Maass
NC-TR-95-004:
Degree of Approximation Results for Feedforward
Networks Approximating Unknown Mappings and Their Derivatives
Kurt Hornik, Maxwell Stinchcombe, Halbert White, Peter Auer
NC-TR-95-005:
Simulating Access to Hidden Information while
Learning
Peter Auer, Philip M. Long
NC-TR-95-006:
A Stop Criterion for the Boltzmann Machine
Learning Algorithm
Berthold Ruf
NC-TR-95-007:
VC-Dimensions for Graphs
Evangelos Kranakis, Danny Krizanc, Berthold Ruf, Jorge Urrutia,
Gerhard J. Woeginger
NC-TR-95-008:
Computing the Maximum Bichromatic Discrepancy,
with applications to
Computer Graphics and Machine Learning
David P. Dobkin, Dimitrios Gunopulos, Wolfgang Maass
NC-TR-95-009:
A Finite Automaton Learning System using Genetic
Programming
Herman Ehrenburg, Jeroen van Maanen
NC-TR-95-010:
On Specifying Boolean Functions by Labelled
Examples
Martin Anthony, Graham Brightwell, John Shawe-Taylor
NC-TR-95-011:
Classification by Polynomial Surfaces
Martin Anthony
NC-TR-95-012:
On the relations between discrete and continuous
complexity theory
Klaus Meer
NC-TR-95-013:
Learnability of Kolmogorov-Easy Circuit Expressions
Via Queries
Jose L. Balcazar, Harry Buhrman, Montserrat Hermo
NC-TR-95-014:
Grundlagen der reellen Komplexitätstheorie
Klaus Meer
NC-TR-95-015:
Computability and complexity over the reals
Paolo Boldi
NC-TR-95-016:
Probably Approximately Optimal Satisficing
Strategies
Russell Greiner, Pekka Orponen
NC-TR-95-017:
Identification of the Human Arm Kinetics using
Dynamic Recurrent Neural Networks
Jean-Philippe DRAYE, Guy CHERON, Marc BOURGEOIS, Davor PAVISIC,
Gaëtan LIBERT
NC-TR-95-018:
On real Turing machines that toss coins
Felipe Cucker, Universitat Pompeu Fabra, Marek Karpinski, Pascal Koiran,
Thomas Lickteig, Kai Werther
NC-TR-95-019:
An Algebraic Characterization of Tractable
Constraints
Peter Jeavons
NC-TR-95-020:
An incremental neural classifier on a MIMD
computer
Arnulfo Azcarraga, Helene Paugam-Moisy and Didier Puzenat
NC-TR-95-021:
Model Selection for Neural Networks: Comparing
MDL and NIC
Guido te Brake, Joost N. Kok, Paul M.B. Vitanyi
NC-TR-95-022:
Option price forecasting using artificial
neural networks
A. Fiordaliso
NC-TR-95-023:
PAC Learning and Artificial Neural Networks
Martin Anthony and Norman Biggs
NC-TR-95-024:
Graphs and Artificial Neural Networks
Martin Anthony
NC-TR-95-025:
The Vapnik-Chervonenkis Dimension of a Random
Graph
Martin Anthony, Graham Brightwell, Colin Cooper
NC-TR-95-026:
Probabilistic Decision Trees and Multilayered
Perceptrons
Pascal Bigot and Michel Cosnard
NC-TR-95-027:
A characterization of the existence of energies
for neural networks
Michel Cosnard, Eric Gole
NC-TR-95-028:
Improvement of Gradient Descent based Algorithms
Training Multilayer
Perceptrons with an Evolutionnary Initialization
Cedric Gegout
NC-TR-95-029:
The Curse of Dimensionality and the Perceptron
Algorithm
Jyrki Kivinen, Manfred K. Warmuth
NC-TR-95-030:
Identifying Regular Languages over Partially-Commutative
Monoids
Claudio Ferretti, Giancarlo Mauri
NC-TR-95-031:
A Comparative Study For Forecasting Intra-daily
Exchange Rate Data
Sabine P Toulson
NC-TR-95-032:
Characterizations of Learnability for Classes
of
{0,...,n}-valued Functions
Shai Ben-David, Nicolò Cesa-Bianchi, David Haussler, Philip M. Long
NC-TR-95-033:
Constructing Computationally Efficient Bayesian
Models via Unsupervised Clustering
Petri Myllymüki and Henry Tirri
NC-TR-95-034:
Mapping Bayesian Networks to Boltzmann Machines
Petri Myllymäki
NC-TR-95-035:
A MINIMAL LENGTH ENCODING SYSTEM
Tony Bellotti, Alex Gammerman
NC-TR-95-036:
Techniques in Neural Learning
Pascal Koiran, John Shawe-Taylor
NC-TR-95-037:
$\P\neq \NP$ over the non standard reals implies
$\P\neq \NP$ over $\R$
Christian Michaux
NC-TR-95-038:
Computing with Truly Asynchronous Threshold
Logic Networks
Pekka Orponen
NC-TR-95-040:
Descriptive Complexity Theory over the Real
Numbers
Erich Grädel and Klaus Meer
NC-TR-95-041:
A General Feedforward Neural Network Model
Cedric GEGOUT, Bernard GIRAU and Fabrice ROSSI
NC-TR-95-042:
Knowledge Extraction From Neural Networks
: A Survey
R. Baron
NC-TR-95-043:
On-line Learning with Malicious Noise and
the Closure Algorithm
Peter Auer, Nicolò Cesa-Bianchi
NC-TR-95-044:
Neural Networks with Quadratic VC Dimension
Pascal Koiran, Eduardo D. Sontag
NC-TR-95-045:
Learning Internal Representations (Short Version)
Jonathan Baxter
NC-TR-95-046:
Learning Model Bias
Jonathan Baxter
NC-TR-95-047:
The Canonical Metric for Vector Quantization
Jonathan Baxter
NC-TR-95-048:
The Complexity of Query Learning Minor Closed
Graph Classes
Carlos Domingo, John Shawe-Taylor
NC-TR-95-049:
Generalisation of A Class of Continuous Neural
Networks
John Shawe-Taylor and Jieyu Zhao
NC-TR-95-050:
Learning Ordered Binary Decision Diagrams
Ricard Gavalda and David Guijarro
NC-TR-95-051:
On the Computational Power of Continuous Time
Neural Networks
Pekka Orponen
NC-TR-95-052:
Computational Machine Learning in Theory and
Praxis
Ming Li, Paul Vitanyi
NC-TR-95-053:
On the relations between distributive computability
and the BSS model
Sebastiano Vigna
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