Statistical machine learning
Methods for efficient resource utilization in statistical machine learning algorithms
... The sharing of memory contents usually incurs a time-memory-trade-off: the more aggressively pages are shared, the more time must be spent to unshare them when they are modified. Blindly optimizing would therefore incur ...
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Some Bayesian and multivariate analysis methods in statistical machine learning and applications
... the statistical machine learning in both the concep- tual and technical ...in statistical learning (Rasmussen and Williams, ...ensemble learning methods such as bagging (Hastie, ...
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Statistical Machine Learning For Information Retrieval Adam Berger pdf
... To the uninitiated, (2.1) might appear a little strange. The goal is to discover the optimal message m ? , but (2.1) suggests doing so by generating (or “predicting”) the input Y . Far more than a simple application of ...
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Statistical Machine Learning Methods for the Large Scale Analysis of Neural Data
... continuous approximations. The latter, that we named Gumbel-Sinkhorn proved the most successful, and is based on two ideas i) distributions in the variational families are reparameterized in terms of a noise ...
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Flexible Statistical Machine Learning Methods for Optimal Treatment Decision.
... treatment regime can individualize treatment and get optimal output for each patient. Different treatments include differences in treatment type and dosage level variation. For some diseases, treatment adjustment is ...
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A Comparative Study of Statistical and Machine Learning Techniques of Background Subtraction in Visual Surveillance
... basic, statistical, machine learning and other ...i.e statistical and machine learning techniques of background subtraction are focused upon in this ...as statistical ...
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Statistical Machine Reordering
... in statistical machine translation ...tistical machine reordering ...for statistical machine translation (SMT) to translate the source language (S) into a reordered source language ...
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Predicting Smoking Status Using Machine Learning Algorithms and Statistical Analysis
... of machine learning approaches to use situational features associated urges to smoke during a quit attempt in order to accurately classify high-urge ...that machine learning can be helpful for ...
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Statistical and Machine Learning Methods for the Classification of Type 2 Diabetes Mellitus
... It is recognised by raised fasting or post prandial blood sugar level. The review of literature shows that one model does not always give good performance in all situations and data frames. The study aimed to address ...
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Pattern Learning for Event Extraction using Monolingual Statistical Machine Translation
... of machine translation, the usage of the person group requires the translation of the full as- sociation, person group plus seed, rather than us- ing the translation model as a look-up table for the seed ...
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Evaluating the Learning Curve of Domain Adaptive Statistical Machine Translation Systems
... The main contribution of this paper is to propose the use of the percentage slope for the evaluation of adapting MT systems, a metric borrowed from the theory on learning curves. For assessing its effec- tiveness, ...
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Learning from Post Editing: Online Model Adaptation for Statistical Machine Translation
... cases it leads to slight improvement and in oth- ers, degradation. It appears that a static but large 4-gram language model often outperforms an in- crementally updated but smaller trigram model. Further, learning ...
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Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
... cal machine translation system is empiri- cally found to improve by using the con- ditional probabilities of phrase pairs com- puted by the RNN Encoder–Decoder as an additional feature in the existing log-linear ...
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Exploring Statistical Parameters of Machine Learning Techniques for Detection and Classification of Brain Tumor
... supervised machine learning algorithm classifiers that show the awareness of multiple peoples in different sectors ...a machine learning technique that works like the processing of human ...
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A Coactive Learning View of Online Structured Prediction in Statistical Machine Translation
... teractive learning from user post-edits (see Cesa- Bianchi et ...online learning techniques to SMT sac- rifices convexity because of latent derivation vari- ables, and because of surrogate translations ...
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Advancing Statistical Inference For Population Studies In Neuroimaging Using Machine Learning
... for statistical parametric mapping analysis of ...discriminative learning is applied to regional neighborhoods towards estimating the multivariate pattern that best reflects the effect of interest, such as ...
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A combined statistical and machine learning approach for single channel speech enhancement
... The feature extraction and the mask estimation are arguably the two most important components for any classification system. All existing works use domain-specific fea- tures that are originally designed for CASA ...
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09401 Abstracts Collection -- Machine learning approaches to statistical dependences and causality
... outside statistical science and internal to any science where statistics is ...in machine learning, on the other hand, have too long focused on a limited set of problems neglecting the mechanisms ...
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A statistical comparison of logistic regression and different bayes classification methods for machine learning
... Recent Machine Learning algorithms are widely available for various purposes. But which classifier is suitable for particular data is not yet defined. To consider this into account, well known classifiers ...
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Precision-mapping and statistical validation of quantitative trait loci by machine learning
... BF, Bayes Factor; BIM, Bayesian Interval Mapping; CIM, composite interval mapping; DArT, diversity arrays tech- nology; DH, doubled haploid; LOD score, logarithm-of- odds ratio in favour[r] ...
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