| dc.contributor.editor | Dudek, Grzegorz | |
| dc.date.accessioned | 2023-07-14T14:30:24Z | |
| dc.date.available | 2023-07-14T14:30:24Z | |
| dc.date.issued | 2023 | |
| dc.identifier | ONIX_20230714_9783036579061_105 | |
| dc.identifier.uri | https://directory.doabooks.org/handle/20.500.12854/101406 | |
| dc.description.abstract | This reprint focuses on applications of machine learning models in a diverse range of fields and problems. It reports substantive results on a wide range of learning methods; discusses the conceptualization of problems, data representation, feature engineering, machine learning models; undertakes critical comparisons with existing techniques; and presents an interpretation of the results. The topics within the chapters of the publication fall into six categories: computer vision, teaching and learning, social media, forecasting, basic problems of machine learning, and other topics. | |
| dc.language | English | |
| dc.subject.classification | thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries | en_US |
| dc.subject.classification | thema EDItEUR::U Computing and Information Technology::UY Computer science | en_US |
| dc.subject.other | robust matrix factorization | |
| dc.subject.other | student grade prediction | |
| dc.subject.other | educational data mining | |
| dc.subject.other | side information graph | |
| dc.subject.other | personal teaching and learning | |
| dc.subject.other | deep multi-target prediction | |
| dc.subject.other | Felder–Silverman learning style | |
| dc.subject.other | adaptive e-learning systems | |
| dc.subject.other | artificial neural network | |
| dc.subject.other | deep learning | |
| dc.subject.other | transfer learning | |
| dc.subject.other | student performance prediction | |
| dc.subject.other | Machine learning analysis | |
| dc.subject.other | sentence modeling | |
| dc.subject.other | topic analysis | |
| dc.subject.other | cross referencing topic | |
| dc.subject.other | machine learning | |
| dc.subject.other | classification | |
| dc.subject.other | preprocessing | |
| dc.subject.other | instance selection | |
| dc.subject.other | data mining | |
| dc.subject.other | predictive analytics | |
| dc.subject.other | sales | |
| dc.subject.other | performance measurement | |
| dc.subject.other | human resources | |
| dc.subject.other | rumor refuter | |
| dc.subject.other | nature language processing | |
| dc.subject.other | XGBoost | |
| dc.subject.other | feature analysis | |
| dc.subject.other | Bitcoin | |
| dc.subject.other | higher order neural network | |
| dc.subject.other | volatility forecasting | |
| dc.subject.other | hybrid models | |
| dc.subject.other | warehouse optimization | |
| dc.subject.other | genetic algorithms | |
| dc.subject.other | crossover | |
| dc.subject.other | construction productivity | |
| dc.subject.other | construction safety | |
| dc.subject.other | synthetic data | |
| dc.subject.other | tracking | |
| dc.subject.other | academic performance | |
| dc.subject.other | course grades | |
| dc.subject.other | grade point average | |
| dc.subject.other | prediction | |
| dc.subject.other | undergraduate | |
| dc.subject.other | cloud detection | |
| dc.subject.other | superpixel segmentation | |
| dc.subject.other | convolutional neural networks | |
| dc.subject.other | support vector machines | |
| dc.subject.other | machine learning algorithms | |
| dc.subject.other | multiple linear regression | |
| dc.subject.other | SVM | |
| dc.subject.other | management | |
| dc.subject.other | social network services | |
| dc.subject.other | image representation | |
| dc.subject.other | local features | |
| dc.subject.other | autoencoder | |
| dc.subject.other | convolutional neural network | |
| dc.subject.other | user generated content | |
| dc.subject.other | sentiment analysis | |
| dc.subject.other | keyword extraction | |
| dc.subject.other | text representation | |
| dc.subject.other | sampling | |
| dc.subject.other | TripAdvisor | |
| dc.subject.other | adaptive camouflage | |
| dc.subject.other | convolutional neural network (CNN) | |
| dc.subject.other | k-means | |
| dc.subject.other | object detection | |
| dc.subject.other | image completion | |
| dc.subject.other | saliency detection | |
| dc.subject.other | social media | |
| dc.subject.other | micro-blogs (Twitter) | |
| dc.subject.other | towards recommending influencers based on topic classification | |
| dc.subject.other | investigation framework | |
| dc.subject.other | comparison of various techniques for topic classification | |
| dc.subject.other | cost-benefit function | |
| dc.subject.other | partial differential equations | |
| dc.subject.other | physics-informed neural network | |
| dc.subject.other | wave equation | |
| dc.subject.other | KdV-Burgers equation | |
| dc.subject.other | KdV equation | |
| dc.subject.other | neural network | |
| dc.subject.other | cyclical learning rate | |
| dc.subject.other | remote sensing | |
| dc.subject.other | scene classification | |
| dc.subject.other | backscatter data | |
| dc.subject.other | lidar ceilometer | |
| dc.subject.other | weather detection | |
| dc.subject.other | online taxi-hailing demand | |
| dc.subject.other | backpropagation neural network | |
| dc.subject.other | extreme gradient boosting | |
| dc.subject.other | real-time prediction | |
| dc.subject.other | climate zone | |
| dc.subject.other | climate change impact | |
| dc.subject.other | Jhelum River Basin | |
| dc.subject.other | Chenab River Basin | |
| dc.subject.other | support vector machine | |
| dc.subject.other | decision tree | |
| dc.subject.other | large-scale dataset | |
| dc.subject.other | relative support distance | |
| dc.subject.other | support vector candidates | |
| dc.subject.other | answer set programming | |
| dc.subject.other | non-deterministic automata induction | |
| dc.subject.other | grammatical inference | |
| dc.subject.other | geopolymer concrete | |
| dc.subject.other | deep neural network | |
| dc.subject.other | ResNet | |
| dc.subject.other | compressive strength | |
| dc.subject.other | fly ash | |
| dc.subject.other | sleep apnea | |
| dc.subject.other | airflow signal | |
| dc.subject.other | Gaussian Mixture Models (GMM) | |
| dc.subject.other | cyber security | |
| dc.subject.other | vulnerability detection | |
| dc.subject.other | word embedding | |
| dc.subject.other | drifter trajectory | |
| dc.subject.other | evolutionary computation | |
| dc.subject.other | NCLS | |
| dc.subject.other | stock performance | |
| dc.subject.other | earning rate | |
| dc.subject.other | volatility | |
| dc.subject.other | heatwaves | |
| dc.subject.other | big data | |
| dc.subject.other | random forest regression model | |
| dc.subject.other | semi-regression | |
| dc.subject.other | early prognosis | |
| dc.subject.other | interpretation | |
| dc.subject.other | COREG algorithm | |
| dc.subject.other | cascaded classifier | |
| dc.subject.other | computer vision | |
| dc.subject.other | construction site management | |
| dc.subject.other | consumer classification | |
| dc.subject.other | over-the-top | |
| dc.subject.other | time-aware classification | |
| dc.subject.other | code auto-completion | |
| dc.subject.other | GPT-2 model | |
| dc.subject.other | advanced design methods | |
| dc.subject.other | mass operator | |
| dc.subject.other | structural stress | |
| dc.subject.other | live prediction | |
| dc.subject.other | vibration test | |
| dc.subject.other | genetic programming | |
| dc.subject.other | parsing expression grammar | |
| dc.subject.other | BiLSTM | |
| dc.subject.other | BERT | |
| dc.subject.other | NLP | |
| dc.subject.other | context-aware | |
| dc.subject.other | LDA | |
| dc.subject.other | LSTM | |
| dc.subject.other | crowdfunding | |
| dc.subject.other | project recommendation system | |
| dc.subject.other | optimization | |
| dc.subject.other | weather nowcasting | |
| dc.subject.other | deep neural networks | |
| dc.subject.other | autoencoders | |
| dc.subject.other | Principal Component Analysis | |
| dc.subject.other | learning classifier systems | |
| dc.subject.other | anticipatory classifier systems | |
| dc.subject.other | reinforcement learning | |
| dc.subject.other | OpenAI gym | |
| dc.subject.other | healthcare | |
| dc.subject.other | COVID | |
| dc.subject.other | time-series predictions | |
| dc.subject.other | ARIMA | |
| dc.subject.other | Prophet | |
| dc.subject.other | GRNN | |
| dc.subject.other | n/a | |
| dc.title | Applied Machine Learning | |
| dc.type | book | |
| oapen.identifier.doi | 10.3390/books978-3-0365-7907-8 | |
| oapen.relation.isPublishedBy | 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 | |
| oapen.relation.isbn | 9783036579061 | |
| oapen.relation.isbn | 9783036579078 | |
| oapen.pages | 808 | |
| oapen.place.publication | Basel | |