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Foundations of Machine Learning
TLDR
This graduate-level textbook introduces fundamental concepts and methods in machine learning. Expand
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Finite-State Transducers in Language and Speech Processing
  • M. Mohri
  • Computer Science
  • Comput. Linguistics
  • 1 June 1997
TLDR
We give a specific study of string-to-weight transducers, including algorithms for determinizing and minizizing these transducers very efficiently, and characterizations. Expand
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  • 100
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Weighted finite-state transducers in speech recognition
TLDR
We survey the use of weighted finite-state transducers (WFSTs) in speech recognition. Expand
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Efficient Large-Scale Distributed Training of Conditional Maximum Entropy Models
TLDR
We analyze and compare the CPU and network time complexity of each of these methods and present a theoretical analysis of conditional maxent models, including a study of the convergence of the mixture weight method, the most resource-efficient technique. Expand
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Domain Adaptation: Learning Bounds and Algorithms
TLDR
This paper addresses the general problem of domain adaptation which arises in a variety of applications where the distribution of the labeled sample available somewhat differs from the test data. Expand
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Algorithms for Learning Kernels Based on Centered Alignment
TLDR
We present a number of novel algorithmic, theoretical, and empirical results for learning kernels based on our notion of centered alignment. Expand
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AUC Optimization vs. Error Rate Minimization
TLDR
The area under an ROC curve (AUC) is a criterion used in many applications to measure the quality of a classification algorithm. Expand
  • 516
  • 48
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OpenFst: A General and Efficient Weighted Finite-State Transducer Library
TLDR
We describe OpenFst, an open-source library for weighted finite-state transducers (WFSTs). Expand
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Multi-armed Bandit Algorithms and Empirical Evaluation
TLDR
The multi-armed bandit problem for a gambler is to decide which arm of a K-slot machine to pull. Expand
  • 401
  • 36
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Two-Stage Learning Kernel Algorithms
TLDR
This paper examines two-stage techniques for learning kernels based on a notion of alignment. Expand
  • 180
  • 36
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