Second-order autoregressive Hidden Markov Model
نویسندگان
چکیده
منابع مشابه
Second-Order Belief Hidden Markov Models
Hidden Markov Models (HMMs) are learning methods for pattern recognition. The probabilistic HMMs have been one of the most used techniques based on the Bayesian model. First-order probabilistic HMMs were adapted to the theory of belief functions such that Bayesian probabilities were replaced with mass functions. In this paper, we present a second-order Hidden Markov Model using belief functions...
متن کاملFiltering Compromised Environment Sensors Using Autoregressive Hidden Markov Model
We propose a method based on autoregressive hidden Markov models (AR-HMM) for filtering out compromised nodes from a sensor network. We assume that sensors are healthy, self-healing and corrupted whereas each node submits a number of readings. A different AR-HMM (A, B, π) is used to describe each of the three types of nodes. For each node, we train an AR-HMM based on the sensor's readings, and ...
متن کاملEfficient mixed-order hidden Markov model inference
Recent studies have shown that high-order hidden Markov models (HMMs) are feasible and useful for spoken language processing. This paper extends the fixed-order versions to ergodic mixedorder HMMs, which allow the modelling of variable-length contexts with significantly less parameters. A novel training procedure automatically infers the number of states and the topology of the HMM from the tra...
متن کاملSecond order hidden Markov models for place recognition: new results
Second order hidden Markov models have been used for a long time in pattern recognition, especially in speech recognition. Their main advantages over other methods (neural networks . . . ) are their capabilities to model noisy temporal signals of variable length. In a previous work, we proposed a new method based on second order hidden Markov models to learn and recognize places in an indoor en...
متن کاملAutomatic word recognition based on second-order hidden Markov models
We propose an extension of the Viterbi algorithm that makes second-order hidden Markov models computationally efficient. A comparative study between first-order (HMM1’s) and second-order Markov models (HMM2’s) is carried out. Experimental results show that HMM2’s provide a better state occupancy modeling and, alone, have performances comparable with HMM1’s plus postprocessing.
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Brazilian Journal of Probability and Statistics
سال: 2017
ISSN: 0103-0752
DOI: 10.1214/16-bjps328