Learning about Jets from Observations of Blazars
Jets, paving their way outward through the inner regions of active nuclei, Compton-interact with the UV radiation from an accretion
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Learning about the chiral structure of the proton from the hyperfine splitting
The complete analytical $O(m_l^3α^5/m_p^2\times (\log{m_π}, \logΔ, \log{m_l}))$ contribution to the Hyperfine splitting is given. It can explain about 2/3 of
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Learning Algorithms for Keyphrase Extraction
Many academic journals ask their authors to provide a list of about five to fifteen keywords, to appear on the
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Learning Analogies and Semantic Relations
We present an algorithm for learning from unlabeled text, based on the Vector Space Model (VSM) of information retrieval, that
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Learning and generalization theories of large committee--machines
The study of the distribution of volumes associated to the internal representations of learning examples allows us to derive the
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Learning class-to-class selectional preferences
Selectional preference learning methods have usually focused on word-to-class relations, e.g., a verb selects as its subject a given nominal
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Learning, competition and cooperation in simple games
The minority model was introduced to study the competition between agents with limited information. It has the remarkable feature that,
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Learning Computational Grammars
This paper reports on the "Learning Computational Grammars" (LCG) project, a postdoc network devoted to studying the application of machine
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Learning Correlations between Linguistic Indicators and Semantic Constraints: Reuse of Context-Dependent Descriptions of Entities
This paper presents the results of a study on the semantic constraints imposed on lexical choice by certain contextual indicators.
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Learning curves for Gaussian process regression: Approximations and bounds
We consider the problem of calculating learning curves (i.e., average generalization performance) of Gaussian processes used for regression. On the
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Learning curves for Soft Margin Classifiers
Typical learning curves for Soft Margin Classifiers (SMCs) learning both realizable and unrealizable tasks are determined using the tools of
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Learning effective amino acid interactions through iterative stochastic techniques
The prediction of the three-dimensional structures of the native state of proteins from the sequences of their amino acids is
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Learning Efficient Disambiguation
This dissertation analyses the computational properties of current performance-models of natural language parsing, in particular Data Oriented Parsing (DOP), points
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Learning Features that Predict Cue Usage
Our goal is to identify the features that predict the occurrence and placement of discourse cues in tutorial explanations in
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Learning for Adaptive Real-time Search
Real-time heuristic search is a popular model of acting and learning in intelligent autonomous agents. Learning real-time search agents improve
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Learning from Scarce Experience
Searching the space of policies directly for the optimal policy has been one popular method for solving partially observable reinforcement
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Learning from the Success of MPI
The Message Passing Interface (MPI) has been extremely successful as a portable way to program high-performance parallel computers. This success
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Learning from Web: Review of Approaches
Knowledge discovery is defined as non-trivial extraction of implicit, previously unknown and potentially useful information from given data. Knowledge extraction
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Learning Hybrid Algorithms for Vehicle Routing Problems
This paper presents a generic technique for improving hybrid algorithms through the discovery of and tuning of meta-heuristics. The idea
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Learning in a Compiler for MINSAT Algorithms
This paper describes learning in a compiler for algorithms solving classes of the logic minimization problem MINSAT, where the underlying
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Learning multilayer perceptrons efficiently
A learning algorithm for multilayer perceptrons is presented which is based on finding the principal components of a correlation matrix
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Learning Nested Agent Models in an Information Economy
We present our approach to the problem of how an agent, within an economic Multi-Agent System, can determine when it
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Learning Policies with External Memory
In order for an agent to perform well in partially observable domains, it is usually necessary for actions to depend
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Learning Polynomial Networks for Classification of Clinical Electroencephalograms
We describe a polynomial network technique developed for learning to classify clinical electroencephalograms (EEGs) presented by noisy features. Using an
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