A comparison of approaches to on-line handwritten character recognition
A comparison of approaches to on-line handwritten character recognition
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Handbook of Learning and Approximate Dynamic Programming (IEEE Press Series on Computational Intelligence)
Support vector machine learning for interdependent and structured output spaces
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Learning as search optimization: approximate large margin methods for structured prediction
ICML '05 Proceedings of the 22nd international conference on Machine learning
Applications of Reinforcement Learning to Structured Prediction
Recent Advances in Reinforcement Learning
| Hi-index | 0.00 |
Many problems in areas such as Natural Language Processing, Information Retrieval, or Bioinformatic involve the generic task of sequence labeling. In many cases, the aim is to assign a label to each element in a sequence. Until now, this problem has mainly been addressed with Markov models and Dynamic Programming.We propose a new approach where the sequence labeling task is seen as a sequential decision process. This method is shown to be very fast with good generalization accuracy. Instead of searching for a globally optimal label sequence, we learn to construct this optimal sequence directly in a greedy fashion. First, we show that sequence labeling can be modelled using Markov Decision Processes, so that several Reinforcement Learning (RL) algorithms can be used for this task. Second, we introduce a new RL algorithm which is based on the ranking of local labeling decisions.