Some Relations Among Stochastic Finite State Networks Used in Automatic Speech Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sequential logic testing and verification
Sequential logic testing and verification
On the Computational Complexity of Approximating Distributions by Probabilistic Automata
Machine Learning - Computational learning theory
Arithmetic built-in self-test for embedded systems
Arithmetic built-in self-test for embedded systems
Automatic Segmentation of Acoustic Musical Signals Using Hidden Markov Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction To Automata Theory, Languages, And Computation
Introduction To Automata Theory, Languages, And Computation
Off-Line Handwritten Word Recognition Using a Hidden Markov Model Type Stochastic Network
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture Classification Using Noncausal Hidden Markov Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICGI '98 Proceedings of the 4th International Colloquium on Grammatical Inference
Learning Stochastic Regular Grammars by Means of a State Merging Method
ICGI '94 Proceedings of the Second International Colloquium on Grammatical Inference and Applications
Inducing Probabilistic Grammars by Bayesian Model Merging
ICGI '94 Proceedings of the Second International Colloquium on Grammatical Inference and Applications
A Parallel Genetic Algorithm for Automatic Generation of Test Sequences for Digital Circuits
HPCN Europe 1996 Proceedings of the International Conference and Exhibition on High-Performance Computing and Networking
Hidden Markov and Independence Models with Patterns for Sequential BIST
VTS '00 Proceedings of the 18th IEEE VLSI Test Symposium
Probabilistic Finite-State Machines-Part I
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Balls of Strings from Edit Corrections
The Journal of Machine Learning Research
A machine learning approach for statistical software testing
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Structural statistical software testing with active learning in a graph
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
Zulu: an interactive learning competition
FSMNLP'09 Proceedings of the 8th international conference on Finite-state methods and natural language processing
| Hi-index | 0.14 |
We present a new model, derived from the Hidden Markov Model (HMM), to learn Boolean vector sequences. Our Hidden Markov Model with Patterns (HMMP) is a simple, hybrid, and interpretable model that uses Boolean patterns to define emission probability distributions attached to states. Vectors consistent with a given pattern are equiprobable, while inconsistent ones have probability zero to be emitted. We define an efficient learning algorithm for this model, which relies on the maximum likelihood principle, and proceeds by iteratively simplifying the structure and updating the parameters of an initial specific HMMP that represents the learning sequences. Each simplification involves merging two states of the current HMMP, while keeping the likelihood as high as possible and the algorithm stops when the HMMP has a sufficiently small structure. HMMPs and our learning algorithm are applied to the Built-in Self-Test (BIST) for integrated circuits, which is one of the key microelectronic problems. An HMMP is learned from a test sequence set (computed using a specific tool) that covers most of the potential faults of the circuit at hand. Then, this HMMP is used as test sequence generator. Our experiments, carried out with classical microelectronic benchmark circuits, show that learned HMMPs have a very high fault coverage. Furthermore, their small sizes combined with their simplicity allow these models to be easily implemented on the circuits for self-testing purposes.