Statistical Learning Approaches with Application to Face Detection

2005 ◽  
pp. 91-104
Author(s):  
Emanuele Franceschi ◽  
Francesca Odone ◽  
Alessandro Verri
Keyword(s):  
Statistical Learning ◽  
Face Detection ◽  
Learning Approaches

Comparison of statistical learning approaches for cerebral aneurysm rupture assessment

2019 ◽  
Vol 15 (1) ◽  
pp. 141-150 ◽  
Author(s):  
Felicitas J. Detmer ◽  
Daniel Lückehe ◽  
Fernando Mut ◽  
Martin Slawski ◽  
Sven Hirsch ◽  
...  
Keyword(s):  
Cerebral Aneurysm ◽  
Statistical Learning ◽  
Aneurysm Rupture ◽  
Learning Approaches

Symbolic and Statistical Learning Approaches to Speech Summarization: A Scoping Review

2021 ◽  
pp. 101305
Author(s):  
Dana Rezazadegan ◽  
Shlomo Berkovsky ◽  
Juan C. Quiroz ◽  
A. Baki Kocaballi ◽  
Ying Wang ◽  
...  
Keyword(s):  
Statistical Learning ◽  
Scoping Review ◽  
Learning Approaches ◽  
Speech Summarization

scholarly journals Quantum Chemistry Meets Machine Learning

2019 ◽  
Vol 73 (12) ◽  
pp. 983-989 ◽  
Author(s):  
Alberto Fabrizio ◽  
Benjamin Meyer ◽  
Raimon Fabregat ◽  
Clemence Corminboeuf
Keyword(s):  
Machine Learning ◽  
Statistical Learning ◽  
Quantum Chemical ◽  
Large Scale ◽  
Organic Molecules ◽  
Chemical Properties ◽  
Learning Approaches ◽  
Energy Landscapes ◽  
Free Energy Landscapes ◽  
Large Scale Screening

In this account, we demonstrate how statistical learning approaches can be leveraged across a range of different quantum chemical areas to transform the scaling, nature, and complexity of the problems that we are tackling. Selected examples illustrate the power brought by kernel-based approaches in the large-scale screening of homogeneous catalysis, the prediction of fundamental quantum chemical properties and the free-energy landscapes of flexible organic molecules. While certainly non-exhaustive, these examples provide an intriguing glimpse into our own research efforts.

scholarly journals Statistical learning approaches in diagnosing patients with nontraumatic acute abdomen

2016 ◽  
Vol 24 ◽  
pp. 3685-3697 ◽  
Author(s):  
Gökmen ZARARSIZ ◽  
Hızır Yakup AKYILDIZ ◽  
Dinçer GÖKSÜLÜK ◽  
Selçuk KORKMAZ ◽  
Ahmet ÖZTÜRK
Keyword(s):  
Acute Abdomen ◽  
Statistical Learning ◽  
Learning Approaches

Classification of bioaccumulative and non-bioaccumulative chemicals using statistical learning approaches

2008 ◽  
Vol 12 (3-4) ◽  
pp. 157-169 ◽  
Author(s):  
Xiuli Sun ◽  
Yan Li ◽  
Xianjie Liu ◽  
Jun Ding ◽  
Yonghua Wang ◽  
...  
Keyword(s):  
Statistical Learning ◽  
Learning Approaches

scholarly journals Machine Learning Approaches for Accurate Prediction of Relative Humidity based on Temperature and Wet-Bulb Depression

2020 ◽  
Author(s):  
Mahdi Ghadiri ◽  
Azam Marjani ◽  
Samira Mohammadinia ◽  
Manouchehr Shokri
Keyword(s):  
Relative Humidity ◽  
Statistical Learning ◽  
Air Conditioning ◽  
Fuzzy Inference ◽  
Least Square ◽  
Support Vector ◽  
Cooling Towers ◽  
Learning Approaches ◽  
Inference System ◽  
Data Points

The main parameters for calculation of relative humidity are the wet-bulb depression and dry bulb temperature. In this work, easy-to-used predictive tools based on statistical learning concepts, i.e., the Adaptive Network-Based Fuzzy Inference System (ANFIS) and Least Square Support Vector Machine (LSSVM) are developed for calculating relative humidity in terms of wet bulb depression and dry bulb temperature. To evaluate the aforementioned models, some statistical analyses have been done between the actual and estimated data points. Results obtained from the present models showed their capabilities to calculate relative humidity for divers values of dry bulb temperatures and also wet-bulb depression. The obtained values of MSE and MRE were 0.132 and 0.931, 0.193 and 1.291 for the LSSVM and ANFIS approaches respectively. These developed tools are user-friend and can be of massive value for scientists especially, those dealing with air conditioning and wet cooling towers systems to have a noble check of the relative humidity in terms of wet bulb depression and dry bulb temperatures.

scholarly journals Non-Concatenative Morpheme Segmentation in Adults and Children

2021 ◽  
Author(s):  
Sara Finley ◽  
Elissa Newport
Keyword(s):  
Statistical Learning ◽  
Learning Approaches ◽  
Limited Ability ◽  
Continuous Structure ◽  
School Aged Children ◽  
Adults And Children ◽  
Extended Experiment

While most morphemes in the world’s language involve continuous structure or concatenation (e.g., prefixes and suffixes), many languages show some form of non-adjacent, non-concatenative morphology. Non-concatenative morphology poses a challenge for statistical learning approaches to morpheme segmentation because the combinatorial possibilities greatly increase for non-adjacent dependencies. The present study explores the types of dependencies that human learners (school-aged children and adults) are able to extract from exposure to a miniature, artificial non-concatenative system. In Experiment 1, participants were exposed to 12 CCC ‘roots’ that fit into 72 CVCVC skeletons with a high variety of VV ‘residue’. Experiment 2 extended Experiment 1 to school-aged children (with adult controls). Experiment 3 replicated Experiment 1, but with ‘mixed’ consonant-vowel roots and residues. Across all three experiments, participants were able to recognize familiar items compared to novel items, but had limited ability to generalize the CCC roots to novel items, suggesting a limited ability to parse consonantal roots. Adults were better at generalizing to novel items compared to children.

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