Sciweavers

NIPS
2004
13 years 5 months ago
Theories of Access Consciousness
Theories of access consciousness address how it is that some mental states but not others are available for evaluation, choice behavior, and verbal report. Farah, O'Reilly, a...
Michael D. Colagrosso, Michael C. Mozer
NIPS
2004
13 years 5 months ago
Modeling Conversational Dynamics as a Mixed-Memory Markov Process
In this work, we quantitatively investigate the ways in which a given person influences the joint turn-taking behavior in a conversation. After collecting an auditory database of ...
Tanzeem Choudhury, Sumit Basu
NIPS
2004
13 years 5 months ago
Hierarchical Eigensolver for Transition Matrices in Spectral Methods
We show how to build hierarchical, reduced-rank representation for large stochastic matrices and use this representation to design an efficient algorithm for computing the largest...
Chakra Chennubhotla, Allan D. Jepson
NIPS
2004
13 years 5 months ago
Using Machine Learning to Break Visual Human Interaction Proofs (HIPs)
Machine learning is often used to automatically solve human tasks. In this paper, we look for tasks where machine learning algorithms are not as good as humans with the hope of ga...
Kumar Chellapilla, Patrice Y. Simard
NIPS
2004
13 years 5 months ago
A Machine Learning Approach to Conjoint Analysis
Choice-based conjoint analysis builds models of consumer preferences over products with answers gathered in questionnaires. Our main goal is to bring tools from the machine learni...
Olivier Chapelle, Zaïd Harchaoui
NIPS
2004
13 years 5 months ago
Sub-Microwatt Analog VLSI Support Vector Machine for Pattern Classification and Sequence Estimation
An analog system-on-chip for kernel-based pattern classification and sequence estimation is presented. State transition probabilities conditioned on input data are generated by an...
Shantanu Chakrabartty, Gert Cauwenberghs
NIPS
2004
13 years 5 months ago
Worst-Case Analysis of Selective Sampling for Linear-Threshold Algorithms
We provide a worst-case analysis of selective sampling algorithms for learning linear threshold functions. The algorithms considered in this paper are Perceptron-like algorithms, ...
Nicolò Cesa-Bianchi, Claudio Gentile, Luca ...
NIPS
2004
13 years 5 months ago
Incremental Algorithms for Hierarchical Classification
We study the problem of hierarchical classification when labels corresponding to partial and/or multiple paths in the underlying taxonomy are allowed. We introduce a new hierarchi...
Nicolò Cesa-Bianchi, Claudio Gentile, Andre...
NIPS
2004
13 years 5 months ago
Proximity Graphs for Clustering and Manifold Learning
Many machine learning algorithms for clustering or dimensionality reduction take as input a cloud of points in Euclidean space, and construct a graph with the input data points as...
Miguel Á. Carreira-Perpiñán, ...
NIPS
2004
13 years 5 months ago
Dependent Gaussian Processes
Gaussian processes are usually parameterised in terms of their covariance functions. However, this makes it difficult to deal with multiple outputs, because ensuring that the cova...
Phillip Boyle, Marcus R. Frean