Krause et al., 2008 - Google Patents
Near-optimal sensor placements in Gaussian processes: Theory, efficient algorithms and empirical studies.Krause et al., 2008
View PDF- Document ID
- 13195106274438459070
- Author
- Krause A
- Singh A
- Guestrin C
- Publication year
- Publication venue
- Journal of Machine Learning Research
External Links
Snippet
When monitoring spatial phenomena, which can often be modeled as Gaussian processes (GPs), choosing sensor locations is a fundamental task. There are several common strategies to address this task, for example, geometry or disk models, placing sensors at the …
- 238000000034 method 0 title abstract description 56
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computer systems based on specific mathematical models
- G06N7/005—Probabilistic networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
- G06Q10/063—Operations research or analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
- G06F17/30386—Retrieval requests
- G06F17/30424—Query processing
- G06F17/30533—Other types of queries
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/02—Knowledge representation
- G06N5/022—Knowledge engineering, knowledge acquisition
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/12—Computer systems based on biological models using genetic models
- G06N3/126—Genetic algorithms, i.e. information processing using digital simulations of the genetic system
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/04—Inference methods or devices
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/50—Computer-aided design
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for programme control, e.g. control unit
- G06F9/06—Arrangements for programme control, e.g. control unit using stored programme, i.e. using internal store of processing equipment to receive and retain programme
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Krause et al. | Near-optimal sensor placements in Gaussian processes: Theory, efficient algorithms and empirical studies. | |
US11531886B2 (en) | Bayesian graph convolutional neural networks | |
Nguyen et al. | Epistemic uncertainty sampling | |
Koch et al. | Tuning and evolution of support vector kernels | |
Ghahramani | Bayesian non-parametrics and the probabilistic approach to modelling | |
US7421380B2 (en) | Gradient learning for probabilistic ARMA time-series models | |
US7937264B2 (en) | Leveraging unlabeled data with a probabilistic graphical model | |
Taddy et al. | Bayesian guided pattern search for robust local optimization | |
Ten Broeke et al. | The use of surrogate models to analyse agent-based models | |
Figini et al. | Corporate default prediction model averaging: A normative linear pooling approach | |
Moustapha et al. | Multi-objective robust optimization using adaptive surrogate models for problems with mixed continuous-categorical parameters | |
Hajikolaei et al. | Decomposition for large-scale global optimization based on quantified variable correlations uncovered by metamodelling | |
Wang et al. | Bayesian optimization | |
Long et al. | Methods and applications of clusterwise linear regression: a survey and comparison | |
Ghassemi et al. | Adaptive in situ model refinement for surrogate-augmented population-based optimization | |
Zimmerman et al. | Copula modeling of serially correlated multivariate data with hidden structures | |
He et al. | Stationary-sparse causality network learning | |
Wheatley et al. | Estimation of the Hawkes process with renewal immigration using the EM algorithm | |
Wang | Stochastic and deterministic algorithms for continuous black-box optimization | |
Rapley et al. | Model-based inferences from adaptive cluster sampling | |
Lee et al. | Dual Graph‐Based Bayesian Network Modeling With Rao‐Blackwellization for Seismic Reliability and Complexity Quantification of Network Connectivity | |
Idowu et al. | Machine learning in pervasive computing | |
Capdevila et al. | Experiments with learning graphical models on text | |
Huang et al. | Variable Selection for Prediction in Clinical Research | |
Töpfer et al. | Online ML Self-adaptation in Face of Traps |