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Prediction of Human Phenotype Ontology terms by means of hierarchical ensemble methods

Marco Notaro, Max Schubach, Peter N. Robinson, Giorgio Valentini
2017 BMC Bioinformatics  
flat learning method to be enhanced.  ...  The modular structure of the proposed methods, that consists in a "flat" learning first step and a hierarchical combination of the predictions in the second step, allows the predictions of virtually any  ...  Acknowledgments The authors would like to thank the anonymous reviewers and the editor for their thorough comments and suggestions. 1  ... 
doi:10.1186/s12859-017-1854-y pmid:29025394 pmcid:PMC5639780 fatcat:5aebo3lfvnd6hh7sjckiawgiey

From Supervised to Unsupervised Support Vector Machines and Applications in Astronomy

Fabian Gieseke
2013 Künstliche Intelligenz  
In this context, we will derive an efficient way to preprocess spectroscopic data, which is based on an adaptation of support vector machines, and the benefits of semi-supervised learning schemes for appropriate  ...  A common task in the field of machine learning is the classification of objects. The basis for such a task is usually a training set consisting of patterns and associated class labels.  ...  inference or transductive support vector machines and from Bennett and Demiriz [10] under the name of semi-supervised support vector machines.  ... 
doi:10.1007/s13218-013-0248-1 fatcat:l5vooxszkfckfexzgezeremb2u

Hierarchical Ensemble Methods for Protein Function Prediction

Giorgio Valentini
2014 ISRN Bioinformatics  
In this paper, we provide a comprehensive review of hierarchical methods for protein function prediction based on ensembles of learning machines.  ...  into account the hierarchical relationships between classes.  ...  Acknowledgements The author thanks the reviewers for their comments and suggestions and acknowledges partial support from the PRIN project "Automi e linguaggi formali: aspetti matematici e applicativi"  ... 
doi:10.1155/2014/901419 pmid:25937954 pmcid:PMC4393075 fatcat:i6w56fpbqnekjozm2kdpik635e

Synergy of multi-label hierarchical ensembles, data fusion, and cost-sensitive methods for gene functional inference

Nicolò Cesa-Bianchi, Matteo Re, Giorgio Valentini
2011 Machine Learning  
biomolecular data, the unbalance between positive and negative examples for each class, the complexity of the whole-ontology and genome-wide dimensions.  ...  Our experiments show that key factors for the success of hierarchical ensemble methods are the integration and synergy among multilabel hierarchical, data fusion, and cost-sensitive approaches, as well  ...  Acknowledgements We would like to thank the anonymous reviewers for their comments and suggestions.  ... 
doi:10.1007/s10994-011-5271-6 fatcat:5uvavmu6zjft3g7xvtc6sghsya

Big Data-Driven Pedestrian Analytics: Unsupervised Clustering and Relational Query Based on Tencent Street View Photographs

Fan Xue, Xiao Li, Weisheng Lu, Christopher J. Webster, Zhe Chen, Lvwen Lin
2021 ISPRS International Journal of Geo-Information  
First, VUCCA transductively adopts two pre-trained deep models to TSV photographs for extracting pedestrians and surrounding pixels into generalizable semantic vectors of features, including uncountable  ...  Then, the extracted pedestrians are semantically clustered using the vectors, e.g., for understanding where they usually stand.  ...  supervised learning.  ... 
doi:10.3390/ijgi10080561 fatcat:ve5gzyminfbrvljjfnmzb57skq

Sentiment Analysis [chapter]

2017 Encyclopedia of Machine Learning and Data Mining  
supervised support vector machines, and the average hat loss.  ...  Semi-supervised Support Vector Machines This semi-supervised learning method assumes that the decision boundary f .x/ D 0 is situated in a low-density region (in terms of unlabeled data) between the two  ...  It might then be possible to learn, for example, that taking action action234 in state state42 is worth 6. 2 and leads to state state654321.  ... 
doi:10.1007/978-1-4899-7687-1_100512 fatcat:ce4yyqo2czftzcx2kbauglh3fu

Spike-Timing-Dependent Plasticity [chapter]

2017 Encyclopedia of Machine Learning and Data Mining  
supervised support vector machines, and the average hat loss.  ...  Semi-supervised Support Vector Machines This semi-supervised learning method assumes that the decision boundary f .x/ D 0 is situated in a low-density region (in terms of unlabeled data) between the two  ...  It might then be possible to learn, for example, that taking action action234 in state state42 is worth 6. 2 and leads to state state654321.  ... 
doi:10.1007/978-1-4899-7687-1_774 fatcat:2jprihjaxfbtpb3ttwuuz3u34y

A Review of Deep Learning Algorithms and Their Applications in Healthcare

Hussein Abdel-Jaber, Disha Devassy, Azhar Al Salam, Lamya Hidaytallah, Malak EL-Amir
2022 Algorithms  
It uses machine learning methods such as supervised, semi-supervised, or unsupervised learning strategies to learn automatically in deep architectures and has gained much popularity due to its superior  ...  Deep learning is a type of machine learning that uses artificial neural networks to mimic the human brain.  ...  Acknowledgments: The authors would like to thank the Arab Open University for supporting this research paper. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/a15020071 fatcat:ku5mfuijdjfxxdv7hlkexad7dy

Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods

Eyke Hüllermeier, Willem Waegeman
2021 Machine Learning  
machine learning scholars, and these problems may call for new methodological developments.  ...  AbstractThe notion of uncertainty is of major importance in machine learning and constitutes a key element of machine learning methodology.  ...  We are also grateful for the additional suggestions made by the reviewers.  ... 
doi:10.1007/s10994-021-05946-3 fatcat:6dndhzin5fgnrp4bjfer47mnt4

Uniting Heterogeneity, Inductiveness, and Efficiency for Graph Representation Learning [article]

Tong Chen, Hongzhi Yin, Jie Ren, Zi Huang, Xiangliang Zhang, Hao Wang
2021 arXiv   pre-print
Also, despite the necessity of inductively producing representations for completely new nodes (e.g., in streaming scenarios), few heterogeneous GNNs can bypass the transductive learning scheme where all  ...  With the ubiquitous graph-structured data in various applications, models that can learn compact but expressive vector representations of nodes have become highly desirable.  ...  semi-supervised representation learning. • FastGCN: As a parallelizable model, FastGCN [17] adopts a sampling strategy for mini-batch training while retaining similar performance as GCN. • GraphSAGE  ... 
arXiv:2104.01711v2 fatcat:uozthcnesjbszdf2ciauvvgrai

Network-based methods for outcome prediction in the "sample space" [article]

Jessica Gliozzo
2017 arXiv   pre-print
In this thesis we present the novel semi-supervised network-based algorithm P-Net, which is able to rank and classify patients with respect to a specific phenotype or clinical outcome under study.  ...  We show that network-based methods in the "sample space" can achieve results competitive with classical supervised inductive systems.  ...  Learning SVM Support Vector Machine TOT Total score t-SNE t-Distributed Stochastic Neighbour Embedding .  ... 
arXiv:1702.01268v1 fatcat:hr3qlnuihvgnhh7hwzs3jgsbna

Utilizing graph machine learning within drug discovery and development

Thomas Gaudelet, Ben Day, Arian R Jamasb, Jyothish Soman, Cristian Regep, Gertrude Liu, Jeremy B R Hayter, Richard Vickers, Charles Roberts, Jian Tang, David Roblin, Tom L Blundell (+2 others)
2021 Briefings in Bioinformatics  
Graph machine learning (GML) is receiving growing interest within the pharmaceutical and biotechnology industries for its ability to model biomolecular structures, the functional relationships between  ...  them, and integrate multi-omic datasets — amongst other data types.  ...  Acknowledgments We gratefully acknowledge support from William L. Hamilton, Benjamin Swerner, Lyuba V. Bozhilova and Andrew Anighoro.  ... 
doi:10.1093/bib/bbab159 pmid:34013350 pmcid:PMC8574649 fatcat:qli5weqbsbhlvhhjjmgze4sjou

AI and ML for Human-Robot Cooperation in Intelligent and Flexible Manufacturing [chapter]

Manuel A. Ruiz Garcia, Erwin Rauch, Renato Vidoni, Dominik T. Matt
2021 Implementing Industry 4.0 in SMEs  
This requires safe human–machine interaction (e.g. with collaborative robots) as well as self and environment awareness capabilities to interact autonomously and smartly between humans and machines.  ...  In particular, the chapter provides an overview of appropriate artificial intelligence (AI) and machine learning (ML) concepts, formally introduces the concept of a safety-aware cyber-physical system and  ...  That is, they follow an inductive reasoning (bottom-up paradigm) .  ... 
doi:10.1007/978-3-030-70516-9_3 fatcat:qsewcqqdkzaf7ltwh5qoai4eiu

A/B Testing [chapter]

2017 Encyclopedia of Machine Learning and Data Mining  
A variety of models such as support vector machines, neural networks, Bayesian models, and rule-based systems have been used for classification-based anomaly detection.  ...  For comparisons with other semi-naive techniques, see semi-naive Bayesian learning.  ...  Cross-References Efficient Exploration in Reinforcement Learning Hierarchical Reinforcement Learning Model-Based Reinforcement Learning  ... 
doi:10.1007/978-1-4899-7687-1_100507 fatcat:bg6sszljsrax5heho4glbcbicu

Average-Payoff Reinforcement Learning [chapter]

2017 Encyclopedia of Machine Learning and Data Mining  
A variety of models such as support vector machines, neural networks, Bayesian models, and rule-based systems have been used for classification-based anomaly detection.  ...  For comparisons with other semi-naive techniques, see semi-naive Bayesian learning.  ...  Cross-References Efficient Exploration in Reinforcement Learning Hierarchical Reinforcement Learning Model-Based Reinforcement Learning  ... 
doi:10.1007/978-1-4899-7687-1_100029 fatcat:jub4ulyg45abnf4qgutimczie4
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