Nothing Special   »   [go: up one dir, main page]

Dhamodaran et al., 2019 - Google Patents

Ensampling data prediction using sparse data in mobile intelligent system

Dhamodaran et al., 2019

View PDF
Document ID
12318169036952245299
Author
Dhamodaran S
Lakshmi M
Publication year

External Links

Snippet

Rain is an important source of water. The Indian economy is heavily dependent on agriculture and the livelihood of Indian farmer largely depends on rains. The farms are more dependent on rainfall than any other water resource. As we observe there is a lot of climate …
Continue reading at www.learntechlib.org (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6267—Classification techniques
    • G06K9/6279—Classification techniques relating to the number of classes
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6267—Classification techniques
    • G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
    • G06K9/6247—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6256—Obtaining sets of training patterns; Bootstrap methods, e.g. bagging, boosting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46—Extraction of features or characteristics of the image
    • 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
    • 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
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00—Computer systems based on specific mathematical models

Similar Documents

Publication Publication Date Title
Ye et al. Attention-driven dynamic graph convolutional network for multi-label image recognition
Saatci et al. Bayesian gan
Ge et al. Multi-evidence filtering and fusion for multi-label classification, object detection and semantic segmentation based on weakly supervised learning
Fiaz et al. Tracking noisy targets: A review of recent object tracking approaches
He et al. Cell tracking using deep neural networks with multi-task learning
Romera-Paredes et al. Recurrent instance segmentation
Lin et al. Active and incremental learning for semantic ALS point cloud segmentation
Ducournau et al. Random walks in directed hypergraphs and application to semi-supervised image segmentation
Zheng et al. Improving the generalization ability of deep neural networks for cross-domain visual recognition
Li et al. DeepCotton: in-field cotton segmentation using deep fully convolutional network
Yang et al. Local label descriptor for example based semantic image labeling
Wu et al. Extended agriculture-vision: An extension of a large aerial image dataset for agricultural pattern analysis
Ji et al. Few-shot scene classification of optical remote sensing images leveraging calibrated pretext tasks
Li et al. Exploring deep anomaly detection methods based on capsule net
US7263509B2 (en) Intelligent spatial reasoning
Dhamodaran et al. Ensampling data prediction using sparse data in mobile intelligent system
Liu et al. Random geometric prior forest for multiclass object segmentation
Ettaouil Generalization Ability Augmentation and Regularization of Deep Convolutional Neural Networks Using l1/2 Pooling
Zhang et al. An ant colony optimization algorithm for image edge detection
Wang et al. Semi-supervised crowd counting with spatial temporal consistency and pseudo-label filter
Zhou et al. Superpixel attention guided network for accurate and real-time salient object detection
Habib et al. A comprehensive review of knowledge distillation in computer vision
Bao et al. Contextual vision transformers for robust representation learning
Benabdeslem et al. Feature selection for self-organizing map
Ali et al. Maximum a posteriori approximation of Dirichlet and beta-Liouville hidden Markov models for proportional sequential data modeling