[PDF] Top 20 Activity classification through hidden Markov modeling
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Activity classification through hidden Markov modeling
... The first smart initialization method we discuss is the segmental K-means procedure [43, section 2.3.2] [16]. It works as follows. On the first training cycle, using the provided annotations, the training set is ... See full document
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Robot introspection through learned hidden Markov models
... Our clustering approach is related to that of Oates et al. [15] who considered the problem of clustering the experiences of a robot into qualitatively different action outcomes. Their cluster prototypes, which are ... See full document
64
Hidden Markov models for monitoring circadian rhythmicity in telemetric activity data
... and classification of specific human activity modes such as standing, walking, running, ascending/descending, sitting, cycling ...of activity (see, ...dynamic Markov process which is ... See full document
16
An analysis of pilot whale vocalization activity using hidden Markov models
... vocalization activity is driven by the behavioral states of the whales, which are not directly observable, so that latent-state models are natural candidates for modeling empirical data on ...use ... See full document
32
Discriminative Hidden Markov Modeling with Long State Dependence using a kNN Ensemble
... The second is to estimate the output model: . Ideally, we would have sufficient training data for every event whose conditional probability we wish to calculate. Unfortunately, there is rarely enough training data to ... See full document
7
Modeling MOOC Student Behavior With Two-Layer Hidden Markov Models
... A unique property of our model is its ability to capture transitions between the behavior patterns themselves that are captured by the latent states. In Figure 6a we show the latent state transition diagram for a 4-state ... See full document
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Action classification using a discriminative non parametric hidden Markov model
... action classification problem in which the joint positions of a human in each video frame are ...for modeling this time-series data is a Hidden Markov Model (HMM) that has a discrete ... See full document
6
Classification and Analysis of MOOCs Learner’s State: The Study of Hidden Markov Model
... overall activity participation on the MOOCs plat- form is low, but the active learners are more likely to actively participate in the activity and the course completion score is ... See full document
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IDENTIFYING THE FACTORS OF MODERN DAY STRESS USING MACHINE LEARNING
... an activity recognition system on a smartphone where the uncertain time-series acceleration signal was analyzed using hierarchical hidden Markov ...real-time activity recognition application ... See full document
6
Segmental Hidden Markov Models with Random Effects for Waveform Modeling
... shape-based modeling and classification of waveform data. A segmental hidden Markov model (HMM) is used to characterize waveform shape and shape variation is captured by adding random effects ... See full document
25
Land Cover Classification Using Hidden Markov Models
... the Hidden Markov Models (HMM s) for unsupervised satellite image classification has been ...texture modeling and segmentation (i.e., classification), this is majorly due to their ... See full document
8
Leveraging Hidden Dialogue State to Select Tutorial Moves
... The results support our hypothesis that HMMs, because of their capacity for explicitly representing dialogue structure at an abstract level, perform better than MMs for predicting tutor moves. The results also suggest ... See full document
8
Optimization of Hidden Markov Model using Minimum Message Length Estimator
... Observing the environment and recognizing patterns for the purpose of decision making is fundamental to any scientific enquiry. Pattern recognition is a scientific discipline that enables perception in machines and it ... See full document
5
Boosted and Linked Mixtures of HMMs for Brain-Machine Interfaces
... three hidden states and an observation sequence length T = 10, which corresponds to one second of data (given the 100- millisecond ...The classification results are determined on each data point in the ... See full document
12
Efficient Multilingual Phoneme to Grapheme Conversion Based on HMM
... The novelty lies in modeling the natural language intraword features using the theory of hidden Markov models HMM and performing the conversion using the Viterbi algorithm.. The PTGC sys[r] ... See full document
26
Using Excel to Simulate and Visualize Conditional Heteroskedastic Models
... Hidden Markov models (HMMs) are a widely used collection of statistical ...goes through a sequence of states. These states are unseen (hidden) but what is observed is data from each ... See full document
5
A Comparative Study of Phoneme Recognition using GMM HMM and ANN based Acoustic Modeling
... statistical modeling techniques with an under-lying doubly stochastic process that is not observable (it is hidden), but can only be observed through another set of stochastic processes that produce ... See full document
5
Modeling and Forecasting Corporate Default Counts Using Hidden Markov Model
... a Hidden Markov Model is employed to fit global, ...the Hidden Markov model is useful in distinguishing the periods of expansion from the periods of recession (relative to the points ... See full document
5
Modeling link quality for high speed railway wireless networks based on hidden Markov chain
... We reason that PLR and RTT are appropriate parame- ters to characterize the link quality of HSR wireless networks. First, IP layer performance is the direct reflec- tion of the radio propagation behavior. For instance, a ... See full document
11
Mixed Membership Markov Models for Unsupervised Conversation Modeling
... govern the transitions between text blocks in a se- quence. We generalize the block HMM approach so that there is no longer a one-to-one correspondence between states in the Markov chain and latent dis- course ... See full document
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