Training Reinforcement Neurocontrollers Using the Polytope Algorithm
A new training algorithm is presented for delayed reinforcement learning problems that does not assume the existence of a critic
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Applying Policy Iteration for Training Recurrent Neural Networks
Recurrent neural networks are often used for learning time-series data. Based on a few assumptions we model this learning task
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A Tutorial on the Expectation-Maximization Algorithm Including Maximum-Likelihood Estimation and EM Training of Probabilistic Context-Free Grammars
The paper gives a brief review of the expectation-maximization algorithm (Dempster 1977) in the comprehensible framework of discrete mathematics. In
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Authoring case based training by document data extraction
In this paper, we propose an scalable approach to modeling based upon word processing documents, and we describe the tool
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Autogenic Training With Natural Language Processing Modules: A Recent Tool For Certain Neuro Cognitive Studies
Learning to respond to voice-text input involves the subject's ability in understanding the phonetic and text based contents and his/her
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Automating Coreference: The Role of Annotated Training Data
We report here on a study of interannotator agreement in the coreference task as defined by the Message Understanding Conference
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Classes for Fast Maximum Entropy Training
Maximum entropy models are considered by many to be one of the most promising avenues of language modeling research. Unfortunately,
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Dynamics of Learning with Restricted Training Sets II: Tests and Applications
We apply a general theory describing the dynamics of supervised learning in layered neural networks in the regime where the
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Lexicalized Stochastic Modeling of Constraint-Based Grammars using Log-Linear Measures and EM Training
We present a new approach to stochastic modeling of constraint-based grammars that is based on log-linear models and uses EM
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On-Line Learning with Restricted Training Sets: An Exactly Solvable Case
We solve the dynamics of on-line Hebbian learning in large perceptrons exactly, for the regime where the size of the
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On the Job Training
We propose a new framework for building and evaluating machine learning algorithms. We argue that many real-world problems require an
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Optimization Over Zonotopes and Training Support Vector Machines
We make a connection between classical polytopes called zonotopes and Support Vector Machine (SVM) classifiers. We combine this connection with
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Role of defects in superconducting wires for degradation and training
Intrinsic and extrinsic defects play an important role in training and degradation of superconducting wires and magnets. Various defects along
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Supervised Grammar Induction Using Training Data with Limited Constituent Information
Corpus-based grammar induction generally relies on hand-parsed training data to learn the structure of the language. Unfortunately, the cost of
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Using existing systems to supplement small amounts of annotated grammatical relations training data
Grammatical relationships (GRs) form an important level of natural language processing, but different sets of GRs are useful for different
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A Memory-Based Approach to Learning Shallow Natural Language Patterns
Recognizing shallow linguistic patterns, such as basic syntactic relationships between words, is a common task in applied natural language and
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An Improved k-Nearest Neighbor Algorithm for Text Categorization
k is the most important parameter in a text categorization system based on k-Nearest Neighbor algorithm (kNN).In the classification process,
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Combining a self-organising map with memory-based learning
Memory-based learning (MBL) has enjoyed considerable success in corpus-based natural language processing (NLP) tasks and is thus a reliable method
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Design of a Protein Potential Energy Landscape by Parameter Optimization
We propose an automated protocol for designing the energy landscape of a protein energy function by optimizing its parameters. The
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Detailed Chemical Abundances of Extragalactic Globular Clusters
We outline a method to measure the detailed chemical composition of extragalactic (unresolved) globular clusters (GCs) from echelle spectra of
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Estimation of Stochastic Attribute-Value Grammars using an Informative Sample
We argue that some of the computational complexity associated with estimation of stochastic attribute-value grammars can be reduced by training
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Full Optimization of Linear Parameters of a United Residue Protein Potential
We apply the general protocol of parameter optimization (Lee, J. et al. Phys. Chem. B 2001, 105, 7291) to the
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Information Extraction Using the Structured Language Model
The paper presents a data-driven approach to information extraction (viewed as template filling) using the structured language model (SLM) as
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Mining the Web for Lexical Knowledge to Improve Keyphrase Extraction: Learning from Labeled and Unlabeled Data
Keyphrases are useful for a variety of purposes, including summarizing, indexing, labeling, categorizing, clustering, highlighting, browsing, and searching. The task
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