Learning ON Large Datasets Using Bit-String Trees | Paper Citation Record

Observation 3a363db6-1d66-4eae-955d-ca80c57a6fe3 · outbound

This paper cites Supervised hashing with kernels.

Learning ON Large Datasets Using Bit-String Trees Supervised hashing with kernels

Reference 1

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Observation c62749da-7e78-47e1-8669-2054e611a809 · outbound

This paper cites Similarity search in high dimensions via hashing.

Learning ON Large Datasets Using Bit-String Trees Similarity search in high dimensions via hashing

Reference 2

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Observation 053aab27-b6f5-417a-a9e1-224ee254fbd1 · outbound

This paper cites Similarity estimation techniques from rounding algorithms.

Learning ON Large Datasets Using Bit-String Trees Similarity estimation techniques from rounding algorithms

Reference 3

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Observation 97849fc3-3982-4fcd-8582-de143b90fa49 · outbound

This paper cites Semantic hashing.

Learning ON Large Datasets Using Bit-String Trees Semantic hashing

Reference 4

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Observation 04fc16ee-54a7-445d-8fbf-8c0bdd1dbb31 · outbound

This paper cites Spectral hashing.

Learning ON Large Datasets Using Bit-String Trees Spectral hashing

Reference 5

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Observation 928a516a-3587-4eff-b1ac-256bc2c8c5a8 · outbound

This paper cites Spherical hashing.

Learning ON Large Datasets Using Bit-String Trees Spherical hashing

Reference 6

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Observation 2b86f17e-a3b5-4c56-8a15-a20cf751bce6 · outbound

This paper cites Semi-supervised hashing for large-scale search.

Learning ON Large Datasets Using Bit-String Trees Semi-supervised hashing for large-scale search

Reference 7

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Observation e53eb77e-072b-4b7f-ad44-aa8d1ef402c6 · outbound

This paper cites Hash function learning via codewords.

Learning ON Large Datasets Using Bit-String Trees Hash function learning via codewords

Reference 8

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Observation 3502b36d-7b25-4150-bca4-903cbd6fee9b · outbound

This paper cites Visual saliency guided complex image retrieval.

Learning ON Large Datasets Using Bit-String Trees Visual saliency guided complex image retrieval

Reference 9

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Observation bfdbf4ae-732e-432a-a8a2-0d69ff55939f · outbound

This paper cites Feature extraction and a database strategy for video fingerprinting.

Learning ON Large Datasets Using Bit-String Trees Feature extraction and a database strategy for video fingerprinting

Reference 10

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Observation c89f25cb-579f-450e-b4ff-7d77428e6009 · outbound

This paper cites A robust and fast video copy detection system using content-based fingerprinting.

Learning ON Large Datasets Using Bit-String Trees A robust and fast video copy detection system using content-based fingerprinting

Reference 11

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Observation 58c0e351-1812-438a-a460-48b914b7b5da · outbound

This paper cites Fast matching for video/audio fingerprinting algorithms.

Learning ON Large Datasets Using Bit-String Trees Fast matching for video/audio fingerprinting algorithms

Reference 12

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Observation e2eb86cd-baef-445a-ad71-07cc6437152d · outbound

This paper cites Audio fingerprinting: nearest neighbor search in high dimensional binary spaces.

Learning ON Large Datasets Using Bit-String Trees Audio fingerprinting: nearest neighbor search in high dimensional binary spaces

Reference 13

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Observation 19f344ea-74c1-4581-bdf2-2f8de22bf3bc · outbound

This paper cites Cellfishing.

Learning ON Large Datasets Using Bit-String Trees Cellfishing

Reference 14

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Observation af6bbcd2-09da-4e06-8973-4382e5d9baa8 · outbound

This paper cites Efficient iot-based sensor big data collection--processing and analysis in smart buildings.

Learning ON Large Datasets Using Bit-String Trees Efficient iot-based sensor big data collection--processing and analysis in smart buildings

Reference 15

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Observation baec2ff0-634c-49aa-ae80-612d67802da8 · outbound

This paper cites Security, privacy & efficiency of sustainable cloud computing for big data & iot.

Learning ON Large Datasets Using Bit-String Trees Security, privacy & efficiency of sustainable cloud computing for big data & iot

Reference 16

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Observation 149b78e7-dec0-4ec1-9897-9772d9f03b87 · outbound

This paper cites Four-image encryption scheme based on quaternion fresnel transform, chaos and computer generated hologram.

Learning ON Large Datasets Using Bit-String Trees Four-image encryption scheme based on quaternion fresnel transform, chaos and computer generated hologram

Reference 17

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Observation 2e720b51-31ee-4439-846d-511da886ad11 · outbound

This paper cites Cellatlassearch: a scalable search engine for single cells.

Learning ON Large Datasets Using Bit-String Trees Cellatlassearch: a scalable search engine for single cells

Reference 18

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Observation 75b6d795-a1a7-4429-8f30-bc7f1009f0cd · outbound

This paper cites Approximate nearest neighbors: towards removing the curse of dimensionality.

Learning ON Large Datasets Using Bit-String Trees Approximate nearest neighbors: towards removing the curse of dimensionality

Reference 19

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Observation 8c2b7ead-1989-424f-9d42-aec8bae6d09e · outbound

This paper cites Fast exact search in hamming space with multi-index hashing.

Learning ON Large Datasets Using Bit-String Trees Fast exact search in hamming space with multi-index hashing

Reference 20

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Observation 32418d34-422b-41cb-acaf-0c1070a4aa63 · outbound

This paper cites Fast nearest neighbor search in the hamming space.

Learning ON Large Datasets Using Bit-String Trees Fast nearest neighbor search in the hamming space

Reference 21

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Observation 08e9da66-24a1-4c0b-b953-98e090459bd5 · outbound

This paper cites A fast approximate nearest neighbor search algorithm in the hamming space.

Learning ON Large Datasets Using Bit-String Trees A fast approximate nearest neighbor search algorithm in the hamming space

Reference 22

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Observation d522836e-fc00-4ec0-9870-f5eaeec51d57 · outbound

This paper cites Lsh forest: self-tuning indexes for similarity search.

Learning ON Large Datasets Using Bit-String Trees Lsh forest: self-tuning indexes for similarity search

Reference 23

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Observation e5a6f1a3-c3b7-4392-be7c-d1882fdba140 · outbound

This paper cites Random projection trees and low dimensional manifolds.

Learning ON Large Datasets Using Bit-String Trees Random projection trees and low dimensional manifolds

Reference 24

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Observation d1c37214-ed1e-433a-b0d8-458bdf363f0d · outbound

This paper cites Randomized partition trees for exact nearest neighbor search.

Learning ON Large Datasets Using Bit-String Trees Randomized partition trees for exact nearest neighbor search

Reference 25

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Observation 36470d5c-53c1-4c8f-92e9-aea2af0c4d14 · outbound

This paper cites Multidimensional binary search trees used for associative searching.

Learning ON Large Datasets Using Bit-String Trees Multidimensional binary search trees used for associative searching

Reference 26

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Observation 1fa0d100-8f2a-4e27-b4d3-04569d743d59 · outbound

This paper cites Olshen and oj.

Learning ON Large Datasets Using Bit-String Trees Olshen and oj

Reference 27

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Observation 9f6463cc-5c32-47d3-8ffd-aa9aa084a98d · outbound

This paper cites Random forests.

Learning ON Large Datasets Using Bit-String Trees Random forests

Reference 28

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Observation 8fa85c46-1460-447b-b99b-ad2025ff5d0d · outbound

This paper cites Extremely randomized trees.

Learning ON Large Datasets Using Bit-String Trees Extremely randomized trees

Reference 29

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Observation 063aae6a-670f-44dd-821b-5f7f808e65be · outbound

This paper cites Five balltree construction algorithms.

Learning ON Large Datasets Using Bit-String Trees Five balltree construction algorithms

Reference 30

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Observation db2db464-86a8-4fcd-9aa9-cf01269dfa8b · outbound

This paper cites An optimal algorithm for approximate nearest neighbor searching fixed dimensions.

Learning ON Large Datasets Using Bit-String Trees An optimal algorithm for approximate nearest neighbor searching fixed dimensions

Reference 31

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Observation ac69ef21-e244-4dc8-a98e-219db9e40153 · outbound

This paper cites Birch: an efficient data clustering method for very large databases.

Learning ON Large Datasets Using Bit-String Trees Birch: an efficient data clustering method for very large databases

Reference 32

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Observation f8e07678-22ac-4643-964f-32d55ef8e704 · outbound

This paper cites hdbscan: Hierarchical density based clustering.

Learning ON Large Datasets Using Bit-String Trees hdbscan: Hierarchical density based clustering

Reference 33

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Observation 338944b8-e931-4b9f-a200-df0a5dea1928 · outbound

This paper cites Agglomerative clustering via maximum incremental path integral.

Learning ON Large Datasets Using Bit-String Trees Agglomerative clustering via maximum incremental path integral

Reference 34

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Observation d02c8696-3dd4-48a4-95e7-4def50d81d5c · outbound

This paper cites Isolation forest.

Learning ON Large Datasets Using Bit-String Trees Isolation forest

Reference 35

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Observation 2dfffdb7-775a-4f4e-bf40-1a77ee22e74b · outbound

This paper cites Adaptive random forests for evolving data stream classification.

Learning ON Large Datasets Using Bit-String Trees Adaptive random forests for evolving data stream classification

Reference 36

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Observation 580a4da7-b621-4890-8158-e0aecea09ad6 · outbound

This paper cites Discovery of rare cells from voluminous single cell expression data.

Learning ON Large Datasets Using Bit-String Trees Discovery of rare cells from voluminous single cell expression data

Reference 37

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This paper cites Classifying many-class high-dimensional fingerprint datasets using random forest of oblique decision trees.

Learning ON Large Datasets Using Bit-String Trees Classifying many-class high-dimensional fingerprint datasets using random forest of oblique decision trees

Reference 38

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Observation 856e4f69-d4f8-4101-8386-da6fcad77ce9 · outbound

This paper cites Random forest in remote sensing: A review of applications and future directions.

Learning ON Large Datasets Using Bit-String Trees Random forest in remote sensing: A review of applications and future directions

Reference 39

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Observation 374ced82-20fb-45f4-b86f-ad44b05926b8 · outbound

This paper cites Oblique random forest based on partial least squares applied to pedestrian detection.

Learning ON Large Datasets Using Bit-String Trees Oblique random forest based on partial least squares applied to pedestrian detection

Reference 40

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Observation cb8c5b5d-b786-445c-8866-50e1d4f10ffc · outbound

This paper cites Oblique random forest ensemble via least square estimation for time series forecasting.

Learning ON Large Datasets Using Bit-String Trees Oblique random forest ensemble via least square estimation for time series forecasting

Reference 41

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Observation 25350849-080d-4b24-ad4c-68977e796cb0 · outbound

This paper cites Robust visual tracking using oblique random forests.

Learning ON Large Datasets Using Bit-String Trees Robust visual tracking using oblique random forests

Reference 42

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Observation 8e05c676-03fd-4da7-b173-b9ff3a9a37b3 · outbound

This paper cites Random forests for genomic data analysis.

Learning ON Large Datasets Using Bit-String Trees Random forests for genomic data analysis

Reference 43

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Observation 2f768b53-870d-4919-b12a-faa1d8b1d992 · outbound

This paper cites Resource-efficient machine learning in 2 kb ram for the internet of things.

Learning ON Large Datasets Using Bit-String Trees Resource-efficient machine learning in 2 kb ram for the internet of things

Reference 44

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Observation c31c317e-1859-4877-b466-20713db6f5b5 · outbound

This paper cites Robust head pose estimation using dirichlet-tree distribution enhanced random forests.

Learning ON Large Datasets Using Bit-String Trees Robust head pose estimation using dirichlet-tree distribution enhanced random forests

Reference 45

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Observation 195698f3-faff-47c3-9e89-83bb9d12ece2 · outbound

This paper cites Ferret: a toolkit for content-based similarity search of feature-rich data.

Learning ON Large Datasets Using Bit-String Trees Ferret: a toolkit for content-based similarity search of feature-rich data

Reference 46

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Observation 8029b05f-aa00-4f47-93fc-d5382a263868 · outbound

This paper cites Sizing sketches: a rank-based analysis for similarity search.

Learning ON Large Datasets Using Bit-String Trees Sizing sketches: a rank-based analysis for similarity search

Reference 47

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Observation 51c03e8d-56de-49ef-9c59-88b9db20a67e · outbound

This paper cites A method and server for predicting damaging missense mutations.

Learning ON Large Datasets Using Bit-String Trees A method and server for predicting damaging missense mutations

Reference 48

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Observation abe8f7f7-83f8-4c03-9f29-6f608c64f505 · outbound

This paper cites Sift missense predictions for genomes.

Learning ON Large Datasets Using Bit-String Trees Sift missense predictions for genomes

Reference 49

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Observation 71e9c9d8-4c64-41a8-845c-87b58b4a7e4d · outbound

This paper cites Disease variant prediction with deep generative models of evolutionary data.

Learning ON Large Datasets Using Bit-String Trees Disease variant prediction with deep generative models of evolutionary data

Reference 50

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Observation 774345e6-b852-4f33-9df4-6a87665bc659 · outbound

This paper cites Efficient Estimation of Word Representations in Vector Space.

Learning ON Large Datasets Using Bit-String Trees Efficient Estimation of Word Representations in Vector Space

Reference 51

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Observation ae173d17-7952-490c-8a1c-6d878606fbb7 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Learning ON Large Datasets Using Bit-String Trees BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 52

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Observation 3f707704-e13c-4376-b761-19907d90094e · outbound

This paper cites Do we need hundreds of classifiers to solve real world classification problems? The Journal of Machine Learning Research , 15(1):3133--3181, 2014.

Learning ON Large Datasets Using Bit-String Trees Do we need hundreds of classifiers to solve real world classification problems? The Journal of Machine Learning Research , 15(1):3133--3181, 2014

Reference 53

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Observation 7e903c02-8e19-465b-95bc-189fe4dc69a9 · outbound

This paper cites Hashing for Similarity Search: A Survey.

Learning ON Large Datasets Using Bit-String Trees Hashing for Similarity Search: A Survey

Reference 54

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Observation 4254f670-8188-4f90-a2bb-e2f979eedf87 · outbound

This paper cites Approximate nearest neighbor search on high dimensional data-experiments, analyses, and improvement.

Learning ON Large Datasets Using Bit-String Trees Approximate nearest neighbor search on high dimensional data-experiments, analyses, and improvement

Reference 55

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Observation 8dcec356-2e08-42f8-9557-6a2f227d4914 · outbound

This paper cites Hbst: A hamming distance embedding binary search tree for feature-based visual place recognition.

Learning ON Large Datasets Using Bit-String Trees Hbst: A hamming distance embedding binary search tree for feature-based visual place recognition

Reference 56

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Observation 9d26c1f1-8fc4-449f-aa9c-58aa9af0fc5e · outbound

This paper cites Locality-sensitive hashing without false negatives.

Learning ON Large Datasets Using Bit-String Trees Locality-sensitive hashing without false negatives

Reference 57

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Observation 68379371-591e-4115-abc6-2e5a07634525 · outbound

This paper cites Scalability and total recall with fast coveringlsh.

Learning ON Large Datasets Using Bit-String Trees Scalability and total recall with fast coveringlsh

Reference 58

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Observation 32663f78-797e-4409-b910-0683f52a2324 · outbound

This paper cites Online nearest neighbor search in binary space.

Learning ON Large Datasets Using Bit-String Trees Online nearest neighbor search in binary space

Reference 59

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Observation ef7635b6-aedc-41b3-a5bb-52b58c5e1146 · outbound

This paper cites Online nearest neighbor search using hamming weight trees.

Learning ON Large Datasets Using Bit-String Trees Online nearest neighbor search using hamming weight trees

Reference 60

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Observation 5ee5fbf6-2f28-4c31-940e-76da37df4db4 · outbound

This paper cites Fast and compact hamming distance index.

Learning ON Large Datasets Using Bit-String Trees Fast and compact hamming distance index

Reference 61

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Observation 2ffae1cd-1557-4c0c-be31-b477870b0554 · outbound

This paper cites o nen, Teemu Pitk \.

Learning ON Large Datasets Using Bit-String Trees o nen, Teemu Pitk \

Reference 62

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Observation 41b2c24b-3410-4329-ab1e-9643322bff32 · outbound

This paper cites dropclust: efficient clustering of ultra-large scrna-seq data.

Learning ON Large Datasets Using Bit-String Trees dropclust: efficient clustering of ultra-large scrna-seq data

Reference 63

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Observation 8951c41c-00dc-42ae-ace8-b7d28341c697 · outbound

This paper cites dropclust2: An r package for resource efficient analysis of large scale single cell rna-seq data.

Learning ON Large Datasets Using Bit-String Trees dropclust2: An r package for resource efficient analysis of large scale single cell rna-seq data

Reference 64

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Observation 88e079cc-3bfb-4d6d-a01f-e605e5906563 · outbound

This paper cites Cellfishing.

Learning ON Large Datasets Using Bit-String Trees Cellfishing

Reference 65

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Learning ON Large Datasets Using Bit-String Trees The evolving concept of cell identity in the single cell era

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Learning ON Large Datasets Using Bit-String Trees Defining cell types and states with single-cell genomics

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Learning ON Large Datasets Using Bit-String Trees Cell state transitions: definitions and challenges

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Learning ON Large Datasets Using Bit-String Trees Scalable nearest neighbor algorithms for high dimensional data

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Learning ON Large Datasets Using Bit-String Trees A single-cell transcriptomic map of the human and mouse pancreas reveals inter-and intra-cell population structure

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Learning ON Large Datasets Using Bit-String Trees Cell type atlas and lineage tree of a whole complex animal by single-cell transcriptomics

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Learning ON Large Datasets Using Bit-String Trees Comprehensive classification of retinal bipolar neurons by single-cell transcriptomics

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Learning ON Large Datasets Using Bit-String Trees Deep discrete supervised hashing

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Learning ON Large Datasets Using Bit-String Trees Supervised hashing with latent factor models

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Learning ON Large Datasets Using Bit-String Trees Asymmetric deep supervised hashing

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Learning ON Large Datasets Using Bit-String Trees Combi: Compressed binary search tree for approximate k-nn searches in hamming space

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Learning ON Large Datasets Using Bit-String Trees Ensemble methods in machine learning

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Learning ON Large Datasets Using Bit-String Trees Greedy function approximation: a gradient boosting machine

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Learning ON Large Datasets Using Bit-String Trees Xgboost: A scalable tree boosting system

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Learning ON Large Datasets Using Bit-String Trees Oc1: A randomized algorithm for building oblique decision trees

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Learning ON Large Datasets Using Bit-String Trees A system for induction of oblique decision trees

Reference 81

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Learning ON Large Datasets Using Bit-String Trees On oblique random forests

Reference 82

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Learning ON Large Datasets Using Bit-String Trees Hhcart: An oblique decision tree

Reference 83

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Learning ON Large Datasets Using Bit-String Trees Guided Random Forest and its application to data approximation

Reference 84

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Learning ON Large Datasets Using Bit-String Trees Classification and regression trees

Reference 85

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Learning ON Large Datasets Using Bit-String Trees A support vector machine approach to decision trees

Reference 86

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Learning ON Large Datasets Using Bit-String Trees A pyramidal delayed perceptron

Reference 87

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Learning ON Large Datasets Using Bit-String Trees Binary classification by svm based tree type neural networks

Reference 88

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Learning ON Large Datasets Using Bit-String Trees Mml inference of oblique decision trees

Reference 89

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Learning ON Large Datasets Using Bit-String Trees Decision forests with oblique decision trees

Reference 90

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Learning ON Large Datasets Using Bit-String Trees An information measure for classification

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Learning ON Large Datasets Using Bit-String Trees Hybrid extreme point tabu search

Reference 92

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Learning ON Large Datasets Using Bit-String Trees Decision-tree-based multiclass support vector machines

Reference 93

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Learning ON Large Datasets Using Bit-String Trees An improved algorithm for decision-tree-based svm

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Learning ON Large Datasets Using Bit-String Trees Geometric decision tree

Reference 95

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Learning ON Large Datasets Using Bit-String Trees Multisurface proximal support vector machine classification via generalized eigenvalues

Reference 96

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Learning ON Large Datasets Using Bit-String Trees Oblique decision tree ensemble via multisurface proximal support vector machine

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Learning ON Large Datasets Using Bit-String Trees Rotation forest: A new classifier ensemble method

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Learning ON Large Datasets Using Bit-String Trees An experimental study on rotation forest ensembles

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Learning ON Large Datasets Using Bit-String Trees CO2 Forest: Improved Random Forest by Continuous Optimization of Oblique Splits

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