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[PDF] Top 20 Semi-Supervised Pattern Recognition and Machine Learning for Eye-Tracking

Has 10000 "Semi-Supervised Pattern Recognition and Machine Learning for Eye-Tracking" found on our website. Below are the top 20 most common "Semi-Supervised Pattern Recognition and Machine Learning for Eye-Tracking".

Semi-Supervised Pattern Recognition and Machine Learning for Eye-Tracking

Semi-Supervised Pattern Recognition and Machine Learning for Eye-Tracking

... This processing is performed prior to using an edge-sensitive Hough circle detector. The approach can easily be adapted for other domains where image segmentation is desirable, or inverted for use when sub-pixel ... See full document

256

Eye Tracking for Password Authentication using Machine Learning

Eye Tracking for Password Authentication using Machine Learning

... an eye tracking ...the eye location across sequential image ...a machine learning approach which can be used for detecting the eye pupil ... See full document

6

Reliable pattern recognition system with novel semi-supervised learning approach

Reliable pattern recognition system with novel semi-supervised learning approach

... In order to achieve a high level of accuracy, researchers have explored different methodologies in different stages of pattern recognition. For example, in the pre-processing stage, normalization, ... See full document

133

Machine Learning on the Cloud for Pattern Recognition

Machine Learning on the Cloud for Pattern Recognition

... tracked objects have moved or not. If a candidate had a high enough similarity to a tracked object, then the tracked object was assumed to be the same as the candidate and the tracked object was updated with the ... See full document

51

SEMI-SUPERVISED MACHINE LEARNING APPROACH FOR DDOS DETECTION

SEMI-SUPERVISED MACHINE LEARNING APPROACH FOR DDOS DETECTION

... protects server resources and ensures that online services are ready to surf the genuine client. Mitigation. )e mitigation phase is applied when an attack occurs, and a suitable security countermeasure is executed to ... See full document

15

LabelForest: Non-Parametric Semi-Supervised Learning for Activity Recognition

LabelForest: Non-Parametric Semi-Supervised Learning for Activity Recognition

... Activity recognition is central to many motion analysis ap- plications ranging from health assessment to ...ity recognition models. Semi-supervised learning has tradi- tionally been a ... See full document

8

Dual Semi-Supervised Learning for Facial Action Unit Recognition

Dual Semi-Supervised Learning for Facial Action Unit Recognition

... For semi-supervised AU recognition scenarios without expressions, label smoothness or AU dependencies are ex- ploited to handle missing ...AU recognition. This method can be naturally extended ... See full document

8

Cost-Sensitive Support Vector Machine for Semi-Supervised Learning

Cost-Sensitive Support Vector Machine for Semi-Supervised Learning

... URL http://dx.doi.org/10.1016/j.neucom.2011.02.011 [24] Z. Qi, Y. Tian, S. Yong, E ffi cient railway tracks detection and turnouts recognition method using hog features, Neural Computing & Applicationsdoi:10.1007 / ... See full document

6

Supervised and Semi Supervised Sequence Learning for Recognition of Requisite Part and Effectuation Part in Law Sentences

Supervised and Semi Supervised Sequence Learning for Recognition of Requisite Part and Effectuation Part in Law Sentences

... various supervised machine learning ...sequence learning models are suitable for RRE tasks and unlabled data also significantly contribute to the performance of RRE ... See full document

9

Detecting Outliers with Semi-Supervised Machine Learning : a Fraud Prediction Application

Detecting Outliers with Semi-Supervised Machine Learning : a Fraud Prediction Application

... Abnormal pattern prediction has received a great deal of attention from both academia and industry, with applications that range from fraud, terrorism and intrusion detection to sensor events, medical diagnoses, ... See full document

33

Investigation on human activity recognition based on supervised machine learning algorithms

Investigation on human activity recognition based on supervised machine learning algorithms

... In this paper, out of various feature extraction methods in computer vision, the Histogram of Oriented Gradients (HOG), Local Binary pattern and Bag Of features method were chosen for carrying of the feature ... See full document

7

Demonstration of Palm Vein Pattern Biometric Recognition by Machine Learning

Demonstration of Palm Vein Pattern Biometric Recognition by Machine Learning

... 6. Recognition accuracy with SVM ...binary pattern (LBP) and its recognition rate by K-nearest neighbor (KNN) and Support Vector Machine ...both machine learning methods can be ... See full document

8

Semi Supervised Learning for Neural Machine Translation

Semi Supervised Learning for Neural Machine Translation

... Since parallel corpora are usually limited in quantity, quality, and coverage, espe- cially for low-resource languages, it is appealing to exploit monolingual corpora to improve NMT. We propose a semi- ... See full document

10

Supervised machine learning for audio emotion recognition

Supervised machine learning for audio emotion recognition

... Emotion Recognition has become and established research sub-domain of Music Information ...Emotion Recognition, which focuses upon detection of emotional stimuli resulting from non-musical ...two ... See full document

15

Distribution-Based Semi-Supervised Learning for Activity Recognition

Distribution-Based Semi-Supervised Learning for Activity Recognition

... Supervised learning methods have been widely applied to ac- tivity ...Distribution-based Semi-Supervised Learning, to tackle the aforementioned ...through semi-supervised ... See full document

8

Deep Generative Models for Semi-Supervised Machine Learning

Deep Generative Models for Semi-Supervised Machine Learning

... art semi-supervised probabilistic machine learning framework that can capture the unique patterns and cluster them accordingly to their respective ...a supervised classifier, learned ... See full document

156

A semi-supervised machine learning framework for microRNA classification

A semi-supervised machine learning framework for microRNA classification

... numerous machine learning methods have been developed to increase classification accuracy and thus reduce validation costs, most methods use supervised learning and thus require large labeled ... See full document

12

A semi-supervised learning approach to arabic named entity recognition

A semi-supervised learning approach to arabic named entity recognition

... entity recognition has a higher recall than ...our semi-supervised sys- tem proved to be easily adaptable when extending the NE ...any semi-supervised ap- ... See full document

9

A Semi-supervised Learning Approach to Arabic Named Entity Recognition

A Semi-supervised Learning Approach to Arabic Named Entity Recognition

... Final Pattern The rationale behind this is to increase the gen- erality of the patterns by making them shorter in length, thus increasing their ability to collect more candidate NEs in the matching process against ... See full document

9

Semi-supervised learning

Semi-supervised learning

... [Global]: supervised learner trained on all of the labeled data, ignoring unlabeled ...our semi- supervised learner that discovers the decision sets us- ing unlabeled data, then trains one ... See full document

8

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