Advances in Information Retrieval [[electronic resource] ] : 24th BCS-IRSG European Colloquium on IR Research Glasgow, UK, March 25-27, 2002 Proceedings / / edited by Fabio Crestani, Mark Girolami, C.J.van Rijsbergen

• Record Nr.: UNISA996465572803316
• Pubbl/distr/stampa: Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2002
• ISBN: 3-540-45886-7
• Edizione: [1st ed. 2002.]
• Descrizione fisica: 1 online resource (XIII, 366 p.)
• Collana: Lecture Notes in Computer Science, , 0302-9743 ; ; 2291
• Disciplina: 005.74
• Soggetti: Data structures (Computer science); Information storage and retrieval; Database management; Artificial intelligence; Application software; Natural language processing (Computer science); Data Structures and Information Theory; Information Storage and Retrieval; Database Management; Artificial Intelligence; Information Systems Applications (incl. Internet); Natural Language Processing (NLP)
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Bibliographic Level Mode of Issuance: Monograph
• Nota di bibliografia: Includes bibliographical references at the end of each chapters and index.
• Nota di contenuto: Multimedia -- Evaluating a Melody Extraction Engine -- Organising and Searching Partially Indexed Image Databases -- Combining Features for Content-Based Sketch Retrieval — A Comparative Evaluation of Retrieval Performance -- The Web -- Combining Web Document Representations in a Bayesian Inference Network Model Using Link and Content-Based Evidence -- An Improved Computation of the PageRank Algorithm -- Serving Enhanced Hypermedia Information -- Query Modification -- The Use of Implicit Evidence for Relevance Feedback in ; Web Retrieval -- Subject Knowledge, Source of Terms, and Term Selection in Query Expansion: An Analytical Study -- Automatic Profile Reformulation Using a Local Document Analysis -- Soft Computing -- A Study on Using Genetic Niching for Query Optimisation in Document Retrieval -- Concept Based Adaptive IR Model Using FCA-BAM Combination for Concept Representation and Encoding -- Models -- A Layered Bayesian Network Model for Document Retrieval -- Term Frequency Normalization via Pareto Distributions -- Optimal Mixture Models in IR -- Categorization -- Text Categorization: An Experiment Using Phrases -- A Hierarchical Model for Clustering and Categorising Documents -- Uncertainty-Based Noise Reduction and Term Selection in Text Categorization -- Structured Documents -- A Graphical User Interface for Structured Document Retrieval -- The Accessibility Dimension for Structured Document Retrieval -- Cross-Language -- Building Bilingual Dictionaries from Parallel Web Documents -- Translation-Based Indexing for Cross-Language Retrieval -- Interactive Systems -- A Retrospective Evaluation Method for Exact-Match and Best-Match Queries Applying an Interactive Query Performance Analyser -- Genre Classification and Domain Transfer for Information Filtering.
• Sommario/riassunto: The annual colloquium on information retrieval research provides an opportunity for both new and established researchers to present papers describing work in progress or ?nal results. This colloquium was established by the BCS IRSG(B- tish Computer Society Information Retrieval Specialist Group), and named the Annual Colloquium on Information Retrieval Research. Recently, the location of the colloquium has alternated between the United Kingdom and continental Europe. To re?ect the growing European orientation of the event, the colloquium was renamed “European Annual Colloquium on Information Retrieval Research” from 2001. Since the inception of the colloquium in 1979 the event has been hosted in the city of Glasgow on four separate occasions. However, this was the ?rst time that the organization of the colloquium had been jointly undertaken by three separate computer and information science departments; an indication of the collaborative nature and diversity of IR research within the universities of the West of Scotland. The organizers of ECIR 2002 saw a sharp increase in the number of go- quality submissions in answer to the call for papers over previous years and as such 52 submitted papers were each allocated 3 members of the program committee for double blind review of the manuscripts. A total of 23 papers were eventually selected for oral presentation at the colloquium in Glasgow which gave an acceptance rate of less than 45% and ensured a very high standard of the papers presented.

Machine Learning and Principles and Practice of Knowledge Discovery in Databases : International Workshops of ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Revised Selected Papers, Part III / / edited by Rosa Meo, Fabrizio Silvestri

• Record Nr.: UNINA9910983079003321
• Autore: Meo Rosa
• Pubbl/distr/stampa: Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025
• ISBN: 9783031746338; 3031746333
• Edizione: [1st ed. 2025.]
• Descrizione fisica: 1 online resource (594 pages)
• Collana: Communications in Computer and Information Science, , 1865-0937 ; ; 2135
• Altri autori (Persone): SilvestriFabrizio
• Disciplina: 006.31
• Soggetti: Artificial intelligence; Image processing - Digital techniques; Computer vision; Computer engineering; Computer networks; Application software; Computers; Data structures (Computer science); Information theory; Artificial Intelligence; Computer Imaging, Vision, Pattern Recognition and Graphics; Computer Engineering and Networks; Computer and Information Systems Applications; Computing Milieux; Data Structures and Information Theory
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di contenuto: -- XAI-TS: Explainable AI for Time Series: Advances and Applications. -- Introducing the Attribution Stability Indicator: a Measure for Time Series XAI Attributions. -- LMFD: Latent Monotonic Feature Discovery. -- LinC: Explaining Time Series Clusterings with User-Provided ; Constraints. -- Explainable Long- and Short-term Pattern Detection in Projected Sequential Data. -- XKDD 2023: 5th International Workshop on eXplainable Knowledge Discovery in Data Mining. -- Matching the expert’s knowledge via a counterfactual-based feature importance measure. -- Explaining Fatigue in Runners Using Time Series Analysis on Wearable Sensor Data. -- Wave Top-k Random-d Family Search: How to Guide an Expert in a Structured Pattern Space. -- Diffusion-based Visual Counterfactual Explanations - Towards Systematic Quantitative Evaluation. -- Exploring gender bias in misclassification with clustering and local explanations. -- Are Generative-based Graph Counterfactual Explainers Worth It?. -- FIPER: a Visual-based Explanation Combining Rules and Feature Importance. -- Manipulation Risks in Explainable AI: The Implications of the Disagreement Problem. -- Using Graph Neural Networks for the Detection and Explanation of Network Intrusions. -- Game Theoretic Explanations for Graph Neural Networks. -- From Black Box to Glass Box: Evaluating the Faithfulness of Process Predictions with GCNNs. -- A New Class of Intelligible Models for Tabular Learning. -- Deep Learning for Sustainable Precision Agriculture. -- Plant Disease Detection using Deep Learning: A. -- Proof of Concept on Pear Leaf Disease Detection. -- Modelling Solar PV Adoption in Irish Dairy Farms using Agent-Based Modelling. -- Deep Networks based Approach for Automatic Counting Panicles on UAV captured Paddy RGB Imagery. -- The ACRE Crop-Weed Dataset for Benchmarking Weed Detection Models on Maize and Beans Fields. -- Integrating Renewable Energy in Agriculture: A Deep Reinforcement Learning-based Approach. -- Knowledge Guided Machine Learning. -- Unsupervised Ontology- and Taxonomy Construction through Hyperbolic Relational Domains and Ranges. -- A Filter-based Neural ODE Approach for Modelling Natural Systems with Prior Knowledge Constraints. -- Towards Automatically Refining Low-Quality Domain Knowledge: A Case Study in Healthcare. -- Lorentz-invariant augmentation for high-energy physics deep learning models. -- Discovering SpatioTemporal Warning Contexts from Non-Emergency Call Reports. -- SEEDOT: Tool for Enhancing Sentiment Lexicon with Machine Learning. -- MACLEAN: MAChine Learning for EArth ObservatioN. -- Detection and semantic description of changes in Earth Observation Time Series data. -- Low-rank hierarchical clustering of PRISMA hyperspectral images to identify burned areas. -- Next day fire prediction via semantic segmentation. -- Robust Burned Area Delineation through Multitask Learning. -- Burnt area extraction from high-resolution satellite images based on anomaly detection. -- Seasonal average temperature forecast with the AutoGluonTS modern autoML tool. -- MLG: Mining and Learning with Graphs. -- Curvature-based Pooling within Graph Neural Networks. -- Finding coherent node groups in directed graphs. -- Neuro Explicit AI and Expert Informed ML for Engineering and Physical Sciences. -- Constructing Neural Forms for Hard-Constraint PINNs with Complex Dirichlet Boundaries. -- Enhancing generability: AutoML for robust denoising of strong gravitational lens systems. -- Data-Efficient Interactive Multi-Objective Optimization Using ParEGO. -- New Frontiers in Mining Complex Patterns. -- Striving for Simplicity in Deep Neural Models Trained for Malware Detection. -- On the Effectiveness of Non-negative Matrix Factorization for Text Open-set Recognition. -- Real-time Anomaly Prediction from Cryptocurrency Time Series. -- A Joint Analysis of Trajectory Mining and Process Mining for Smartphone User Behaviour. -- Towards Automation of Pollen Monitoring - Dealing with the Background in Pollen Monitoring Images.
• Sommario/riassunto: The five-volume set CCIS 2133-2137 constitutes the refereed ; proceedings of the workshops held in conjunction with the Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2023, which took place in Turin, Italy, during September 18-22, 2023. The 200 full papers presented in these proceedings were carefully reviewed and selected from 515 submissions. The papers have been organized in the following tracks: Part I: Advances in Interpretable Machine Learning and Artificial Intelligence -- Joint Workshop and Tutorial; BIAS 2023 - 3rd Workshop on Bias and Fairness in AI; Biased Data in Conversational Agents; Explainable Artificial Intelligence: From Static to Dynamic; ML, Law and Society; Part II: RKDE 2023: 1st International Tutorial and Workshop on Responsible Knowledge Discovery in Education; SoGood 2023 – 8th Workshop on Data Science for Social Good; Towards Hybrid Human-Machine Learning and Decision Making (HLDM); Uncertainty meets explainability in machine learning; Workshop: Deep Learning and Multimedia Forensics. Combating fake media and misinformation; Part III: XAI-TS: Explainable AI for Time Series: Advances and Applications; XKDD 2023: 5th International Workshop on eXplainable Knowledge Discovery in Data Mining; Deep Learning for Sustainable Precision Agriculture; Knowledge Guided Machine Learning; MACLEAN: MAChine Learning for EArth ObservatioN; MLG: Mining and Learning with Graphs; Neuro Explicit AI and Expert Informed ML for Engineering and Physical Sciences; New Frontiers in Mining Complex Patterns; Part IV: PharML, Machine Learning for Pharma and Healthcare Applications; Simplification, Compression, Efficiency and Frugality for Artificial intelligence; Workshop on Uplift Modeling and Causal Machine Learning for Operational Decision Making; 6th Workshop on AI in Aging, Rehabilitation and Intelligent Assisted Living (ARIAL); Adapting to Change: Reliable Multimodal Learning Across Domains; AI4M: AI for Manufacturing; Part V: Challenges and Opportunities of Large Language Models in Real-World Machine Learning Applications; Deep learning meets Neuromorphic Hardware; Discovery challenge; ITEM: IoT, Edge, and Mobile for Embedded Machine Learning; LIMBO - LearnIng and Mining for BlOckchains; Machine Learning for Cybersecurity (MLCS 2023); MIDAS - The 8th Workshop on MIning DAta for financial applicationS; Workshop on Advancements in Federated Learning.