[PDF] Top 20 Active Learning for Constrained Dirichlet Process Mixture Models
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Active Learning for Constrained Dirichlet Process Mixture Models
... Compared to standard pool-based AL, when clustering with constraints the possible links be- tween two instances (ignoring transitivity) are C(N, 2) = N (N − 1)/2 (N is the size of the dataset) and there is an equal ... See full document
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Sensor based human activity mining using Dirichlet process mixtures of directional statistical models
... machine learning and data mining techniques to automatically establish the correlation between sensor data and activity ...Markov Models (HMM) and recent deep neural networks are the most popular techniques ... See full document
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Active appearance pyramids for object parametrisation and fitting
... part-based models have shown superior perfor- mance in computer vision tasks including object detection and ...Part Models [7, 8, 9], Constrained Local Models [10, 11, 12] and ... See full document
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TSDPMM: Incorporating Prior Topic Knowledge into Dirichlet Process Mixture Models for Text Clustering
... Table 1 shows the average perplexity values of five runs of 3 models on NIPS datasets. It shows that both TSDPMM-P and TSDPMM-E, lever- aging prior topics from previous learning and “CFP” significantly ... See full document
6
Beating-Time Gestures Imitation Learning for Humanoid Robots
... a Dirichlet Process Mixture Model (DPMM) as front end for a continuous HMM (cHMM) to characterise every beating-time gesture by a set of non-equidistant key ...of models that are more ... See full document
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Nonlinear Models Using Dirichlet Process Mixtures
... linear models has been explored extensively in the field of machine ...supervised learning procedure for models that are comprised of several local models (experts) each handling a subset of ... See full document
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Non-parametric Bayesian mixture of sparse regressions with application towards feature selection for statistical downscaling
... Climate Models (GCMs) are often used for assessing the impacts of climate ...involves learning a regression model that relates these features (predictors) to a climatic variable of interest (predictand) ... See full document
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Microalgae classification using semi-supervised and active learning based on Gaussian mixture models
... the active algorithm analyzes the instances that were not ranked and searches among them for the one that provides the largest information gain for the ...This process is repeated as long as the user finds ... See full document
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PReMiuM : an R package for profile regression mixture models using Dirichlet processes
... In addition to fitting mixtures, potentially linking covariates and responses, it may addition- ally be of interest to determine which covariates actively drive the mixture components, and which share ... See full document
30
Bayesian non-negative factor analysis for reconstructing transcription factor mediated regulatory networks
... analysis models, BNFM con- sists of a sparse loading matrix and a set of correlated non-negative ...is constrained by a sparse prior [16] that directly reflects our existing knowledge of TF ...a ... See full document
14
Multi-Task Learning for Classification with Dirichlet Process Priors
... a mixture distribution, but two issues exist in that work: (i) Extra “high-level” task characteristics, other than the features used for learning the model parameters of individual tasks, are needed to ... See full document
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Unsupervised and Constrained Dirichlet Process Mixture Models for Verb Clustering
... non-parametric models have received a lot of attention in the machine learning commu- ...These models have the attractive property that the number of components used to model the data is not fixed in ... See full document
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A Tightly coupled Unsupervised Clustering and Bilingual Alignment Model for Transliteration
... Bayesian learning have over- come issues with over-fitting allowing for many-to-many alignment in the training of transliteration ...coupled Dirichlet process mixture mod- el (cDPMM) that ... See full document
6
Discovering Morphological Paradigms from Plain Text Using a Dirichlet Process Mixture Model
... ing it a plausible explanation of ¼ as well. (This is the “rich get richer” property of the Chinese restau- rant process; see section 6.6.) Furthermore, certain inflections of b&r ak appear to be especially ... See full document
12
Human Computer Interactive Chinese Word Segmentation: An Adaptive Dirichlet Process Mixture Model Approach
... Zhu et al. (2013) proposed a Kalman filter based human-computer interactive learning mod- el for segmenting Chinese texts depending upon neither lexicon nor any annotated corpus. This approach enables experts to ... See full document
7
Bayesian non parametric inference for Λ coalescents : posterior consistency and a parametric method
... Remark 2. There is an apparent contradiction between the negative conclusion of The- orem 1 and recent positive results [Spence et al., 2016, Theorems 2, 3, 4 and 5] showing that Λ-measures can often be identified from ... See full document
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Integer Linear Programming in NLP Constrained Conditional Models
... After shortly motivating and introducing the general framework, the main part of the tutorial is a methodological presentation of some of the key computational issues studied within CCMs that we will present by looking ... See full document
6
Text Document Clustering Using DPM with Concept and Feature Analysis
... The Dirichlet Process Mixture (DPM) is to develop a model for partitioning the ...clustering process and leads to poor ...a mixture of two components, discriminative words and non ... See full document
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... clustering process rather than pre- estimated, this method is very easy to use and it aint require expensive ...finite mixture model have been successfully applied to the document ... See full document
8
Pattern Based Topics for Document Modelling Using HLA
... the process of filtering but it construct and also adapt the ...machine learning, pattern recognition text mining and data mining, are the foundation for increasing of development of information filters ... See full document
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