Introduction to algorithms
A tutorial on hidden Markov models and selected applications in speech recognition
Readings in speech recognition
Toward a real-time spoken language system using commercial hardware
HLT '90 Proceedings of the workshop on Speech and Natural Language
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Maximum Entropy Markov Models for Information Extraction and Segmentation
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Machine Learning for Sequential Data: A Review
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Fast inference and learning in large-state-space HMMs
ICML '05 Proceedings of the 22nd international conference on Machine learning
Algorithms for Chordal Analysis
Computer Music Journal
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
A conditional model for tonal analysis
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
SPIRAL: efficient and exact model identification for hidden Markov models
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Trip Around the HMPerceptron Algorithm: Empirical Findings and Theoretical Tenets
AI*IA '07 Proceedings of the 10th Congress of the Italian Association for Artificial Intelligence on AI*IA 2007: Artificial Intelligence and Human-Oriented Computing
Tonal Harmony Analysis: A Supervised Sequential Learning Approach
AI*IA '07 Proceedings of the 10th Congress of the Italian Association for Artificial Intelligence on AI*IA 2007: Artificial Intelligence and Human-Oriented Computing
Fast likelihood search for hidden Markov models
ACM Transactions on Knowledge Discovery from Data (TKDD)
Empirical Assessment of Two Strategies for Optimizing the Viterbi Algorithm
AI*IA '09: Proceedings of the XIth International Conference of the Italian Association for Artificial Intelligence Reggio Emilia on Emergent Perspectives in Artificial Intelligence
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In this paper we present a novel algorithm, CarpeDiem. It significantly improves on the time complexity of Viterbi algorithm, preserving the optimality of the result. This fact has consequences on Machine Learning systems that use Viterbi algorithm during learning or classification. We show how the algorithm applies to the Supervised Sequential Learning task and, in particular, to the HMPerceptron algorithm. We illustrate CarpeDiem in full details, and provide experimental results that support the proposed approach.