Supervised learning
Supervised learning with complex spikes and spike-timing-dependent plasticity.
... for supervised learning with complex spikes and STDP; it remains to be seen whether this mechanism is part of learning in Purkinje ...for learning in Purkinje cells: in [80] the synaptic ...
9
Haptic Robot-Environment Interaction for Self-Supervised Learning in Ground Mobility
... self-supervised learning mecha- nisms to ascertain navigation affordances from depth ...memory learning mechanism allied with the haptic interaction point evaluation prioritize interaction points to ...
95
SUPERVISED LEARNING METHODS FOR BANGLA WEB DOCUMENT CATEGORIZATION
... In this paper, we study how information from Bangla online text documents can be categorized using four supervised learning algorithm, namely DT (C4.5), KNN, NB, and SVM. These classification algorithms are ...
13
Automatic identification of bat species using supervised learning
... The composition of the database takes us to an important issue regarding supervised learning, the need for significant amounts of hand-labeled data to reliably train models. This should not constitute a ...
89
Phenotype classification of zebrafish embryos by supervised learning.
... The binary models performed in general really well. Two defects, “Up Curved Tail” and “Necrosed Yolk Sac” ranged between 85–88% agreement with experts. Inspection of the classi- fied images indicated that this relatively ...
20
Predicting progression of mild cognitive impairment to dementia using neuropsychological data: a supervised learning approach using time windows
... machine learning approaches are gaining relevance in dementia research [15, 33], studies includ- ing only NPTs are mostly based on traditional statistical analysis instead of machine ...
15
Res. Biomed. Eng. vol.33 número4
... Department of Research & Scientific Affairs. Skateboarding safety [internet]. Rosemont: AAOS; 2013. [cited 2001 Oct 31]. Available from: http://orthoinfo.aaos.org/topic.cfm?topic=a00273. Dong-Hyun L. Pseudo-Label: ...
8
A Two Step Data Mining Approach for Amharic Text Classification
... One way to reduce the amount of labeled data required is to develop an algorithm that can learn from a small number of labeled examples augmented with a large number of unlabeled examples.The Web contains a huge amount ...
9
MACHINE LEARNING TECHNIQUES USED IN BIG DATA
... machine learning has increased because it works well with large amount and manifold of ...machine learning techniques: supervised learning and unsupervised ...
6
A Novel Classification Algorithm Based on Incremental Semi-Supervised Support Vector Machine.
... Traditional learning schemes could not adequately address this problem due to a lack of dynamic data selection ...human learning process, a novel classification algorithm based on incremental ...
19
An Unsupervised Approach for Mining Multiple Web Databases
... the supervised learning techniques is the requirement for a large number of training ...active learning methods to automatically locate such ambiguous ...
4
Clinics vol.65 número12
... The Orbscan II TM (Bausch & Lomb) is a hybrid system that acquires data through slit-scanning and Placido ring technology. This instrument is able to map multiple ocular surfaces beyond the anterior corneal surface. ...
6
Systematically differentiating functions for alternatively spliced isoforms through integrating RNA-seq data.
... Integrating large-scale functional genomic data has significantly accelerated our understanding of gene functions. However, no algorithm has been developed to differentiate functions for isoforms of the same gene using ...
16
Clinical Relationships Extraction Techniques from Patient Narratives
... For processing the clinical information, a framework is defined which called the Clinical E-Science Framework (CLEF) project [3] for capture, integration and presentation of this information. The project's data resource ...
15
Biomedical information extraction for matching patients to clinical trials
... Even doe this problem could be solved with many different machine learning struc- tures, for this thesis the only structure used for supervised learning was decision trees. But why using decision ...
118
Comparison of Supervised and Unsupervised Learning Algorithms for Pattern Classification
... of learning from ...patterns. Learning behavior of the neural network model enhances the classification ...two learning algorithms namely supervised and unsupervised and investigated its ...
5
Activity recognition from smartphone sensing data
... This thesis aims to meet this new solution, creating an Android application, for the problem of recognizing the activities performed by the user and treating it as a classification problem. To embark into a path that ...
74
CONSTRUCTIVE NEURAL NETWORKS: A REVIEW
... the learning accuracy, the generalization ability and training time of supervised learning in FNNs depend on various factors such as chosen network architecture (number of hidden nodes and connection ...
9
Differential anti-glycan antibody responses in Schistosoma mansoni-infected children and adults studied by shotgun glycan microarray.
... The grouping of individuals in the non-supervised HCA and PCA described above was mainly due to glycan clusters C1 and C3 (Figures 4A and 4B) together forming the majority of the glycans present on the shotgun ...
10
Keep using day-to-day tools in the classroom
... Collaborative learning; Cooperative learning; Interactive learning; Opportunities for in and out of class social learning activities. Confident - High level of optimism - Technologica[r] ...
11