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NeuroCOLT
Technical Reports 1997
NC-TR-97-001
Multilayer neural networks: one or two hidden
layers?
G. Brightwell, C. Kenyon, H. Paugam-Moisy
NC-TR-97-002
Size of multilayer networks for exact learning:
analytic approach
A. Elisseeff and H. Paugam-Moisy
NC-TR-97-003:
Constructing Bayesian finite mixture models
by the EM algorithm
Petri Kontkanen, Petri Myllymäki, Henry Tirri,
NC-TR-97-004:
Comparing Predictive Inference Methods for
Discrete Domains
Petri Kontkanen, Petri Myllymäki, Tom Silander, Henry Tirri, Peter
Grunwald
NC-TR-97-005:
Genetic Fitness Optimization Using Rapidly
Mixing Markov Chains
Paul Vitanyi
NC-TR-97-006:
On the Well-Behavedness of Important Attribute
Evaluation Functions
Tapio Elomaa, Juho Rousu
NC-TR-97-007:
A Note on Non-complete Problems in $NP_{\Re}$
S. Ben-David, K. Meer, C. Michaux
NC-TR-97-008:
Semi-algebraic Complexity -- Additive Complexity
of Matrix Computational Tasks
T. Lickteig, K. Meer
NC-TR-97-009:
Multilayer Perceptrons and Learning
Alberto Bertoni, Paola Campadelli, Nicolò Cesa-Bianchi
NC-TR-97-010:
On Bayes Methods for On-line Boolean Prediction
Nicolò Cesa-Bianchi, David Helmbold, Sandra Panizza
NC-TR-97-011:
Randomized Hypotheses and Minimum Disagreement
Hypotheses
Nicolò Cesa-Bianchi, Paul Fischer, Eli Shamir, Hans Ulrich Simon
NC-TR-97-012:
Learning with Restricted Focus of Attention
Shai Ben-David, Eli Dichterman
NC-TR-97-013:
A PAC Analysis of a Bayesian Estimator
John Shawe-Taylor, Robert Williamson
NC-TR-97-014:
A New Incremental Learning Technique
Nick Dunkin, John Shawe-Taylor, Pascal Koiran
NC-TR-97-015:
Exact Learning of subclasses of CDNF formulas
with membership queries
Carlos Domingo
NC-TR-97-016:
Decision Trees have Approximate Fingerprints
Victor Lavin, Vijay Raghavan
NC-TR-97-017:
Learning Monotone Term Decision Lists
David Guijarro, Victor Lavin, Vijay Raghavan
NC-TR-97-018:
Learning nearly monotone $k$-term DNF
Jorge Castro, David Guijarro, Victor Lavin,
NC-TR-97-019:
$\delta$-uniform BSS Machines
Paolo Boldi, Sebastiano Vigna
NC-TR-97-020:
The Computational Power of Spiking Neurons
Depends on the Shape of the Postsynaptic Potentials
Wolfgang Maass, Berthold Ruf
NC-TR-97-021:
On the Effect of Analog Noise in Discrete-Time
Analog Computations
Wolfgang Maass, Pekka Orponen
NC-TR-97-022:
Networks of Spiking Neurons Can Emulate Arbitrary
Hopfield Nets in Temporal Coding
Wolfgang Maass and Thomas Natschläger
NC-TR-97-023:
The Perceptron algorithm vs. Winnow: linear
vs. logarithmic mistake bounds when few input variables are relevant
Jyrki Kivinen, Manfred Warmuth, Peter Auer
NC-TR-97-024:
Approximating Hyper-Rectangles: Learning and
Pseudo-random Sets
Peter Auer, Philip Long, Aravind Srinivasan
NC-TR-97-025:
On Learning from Multi-Instance Examples:
Empirical Evaluation of a Theoretical Approach
Peter Auer
NC-TR-97-026:
Computing Functions with Spiking Neurons in
Temporal Coding
Berthold Ruf
NC-TR-97-027:
Hebbian Learning in Networks of Spiking Neurons
Using Temporal Coding
Berthold Ruf, Michael Schmitt
NC-TR-97-028:
Overview of Learning Systems produced by NeuroCOLT
Partners
NeuroCOLT Partners
NC-TR-97-029:
On Bayesian Case Matching
Petri Kontkanen, Petri Myllymäki, Tom Silander and Henry Tirri
NC-TR-97-030:
Batch Classifications with Discrete Finite
Mixtures
Petri Kontkanen, Petri Myllymäki, Tom Silander and Henry Tirri
NC-TR-97-031:
Bayes Optimal Lazy Learning
Petri Kontkanen, Petri Myllymäki, Tom Silander and Henry Tirri,
NC-TR-97-032:
On Predictive Distributions and Bayesian Networks
Petri Kontkanen, Petri Myllymäki, Tom Silander and Henry Tirri, Peter
Grunwald,
NC-TR-97-033:
Partial Occam's Razor and its Applications
Carlos Domingo, Tatsuie Tsukiji and Osamu Watanabe,
NC-TR-97-034:
Algorithms for Learning Finite Automata from
Queries: A Unified View
Jose Balcazar, Josep Diaz, Ricard Gavalda, Osamu Watanabe
NC-TR-97-035:
Using Fewer Examples to Simulate Equivalence
Queries
Ricard Gavalda
NC-TR-97-036:
A Dichotomy Theorem for Learning Quantified
Boolean Formulas
Victor Dalmau
NC-TR-97-037:
Discontinuities in Recurrent Neural Networks
Ricard Gavalda, Hava Siegelmannl
NC-TR-97-038:
Using Computational Learning Strategies as
a Tool for Combinatorial Optimization
Andreas Birkendorf and Han Ulrich Simon
NC-TR-97-039:
A Unifying Framework for Invariant Pattern
Recognition
Jeffrey Wood and John Shawe-Taylor
NC-TR-97-040:
Saturation and Stability in the Theory of
Computation over the Reals
Olivier Chapuis, Pascal Koiran
NC-TR-97-041:
A survey on the Grzegorczyk Hierarchy and
its extension through the BSS Model of Computability
Jean-Sylvestre Gakwaya
NC-TR-97-042:
On the Effect of Analog Noise in Discrete-Time
Analog Computations
Wolfgang Maass, Pekka Orponen
NC-TR-97-043:
Analog Neural Nets with Gaussian or other
Common Noise Distributions cannot Recognise Arbitrary Regular Languages
Wolfgang Maass, Eduardo D. Sontag
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