Paper Citation Record · LEDGER

Guided Random Forest and its application to data approximation

As of 21 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 1 inbound Pith citation observation for arXiv:1909.00659.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
1909.00659 v2

Coverage vector

measured 20 of 20 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T05:51:53.641011Z

measured 21 of 21 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T17:14:39.085190Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-15T17:14:40.209906Z

Reference resolution

20 of 20 outbound references displayed

verified exact0
verified fuzzy19
unresolved1
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0bc0765a-7c0d-4d4d-aa04-a7767a64afe6 · outbound

This paper cites Ensemble methods in machine learning,.

Guided Random Forest and its application to data approximation Ensemble methods in machine learning,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:51:53.985334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ecebc72a-902a-4d65-80bd-2a5d4bfc5323 · outbound

This paper cites Do we need hundreds of classiﬁers to solve real world classiﬁca- tion problems?.

Guided Random Forest and its application to data approximation Do we need hundreds of classiﬁers to solve real world classiﬁca- tion problems?

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:51:53.968041Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 6677e37a-fe6c-42e7-9b1a-bce4c90a1127 · outbound

This paper cites Random forests,.

Guided Random Forest and its application to data approximation Random forests,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-14T05:51:53.558097Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 53b7dc93-1ace-4e3f-b16c-3d37bcbf4edc · outbound

This paper cites Random rotation ensembles,.

Guided Random Forest and its application to data approximation Random rotation ensembles,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:51:53.941266Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 7630ca16-7722-42f8-9add-4e5e41d12fd2 · outbound

This paper cites On oblique random forests,.

Guided Random Forest and its application to data approximation On oblique random forests,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:51:53.924783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 2ca5a6dc-27b4-44d8-bbd4-737c1d03ba83 · outbound

This paper cites Nonlinear boosting projections for ensemble construction,.

Guided Random Forest and its application to data approximation Nonlinear boosting projections for ensemble construction,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:51:53.908557Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 7251a194-5e92-4a50-8bd8-0da3e7345fb9 · outbound

This paper cites Rotation forest: A new classiﬁer ensemble method,.

Guided Random Forest and its application to data approximation Rotation forest: A new classiﬁer ensemble method,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:51:53.892534Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 504bd98f-d0cf-4128-a7d3-8f4cd0ce50a8 · outbound

This paper cites An experimental study on rotation forest ensembles,.

Guided Random Forest and its application to data approximation An experimental study on rotation forest ensembles,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:51:53.876646Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5433f8ba-8bd7-4ce1-b55b-5005f4fa0c91 · outbound

This paper cites An empirical evaluation of rotation-based ensemble classiﬁers for customer churn predic- tion,.

Guided Random Forest and its application to data approximation An empirical evaluation of rotation-based ensemble classiﬁers for customer churn predic- tion,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:51:53.861516Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation bcc4787d-9c47-4ece-8e87-3a597125aa5c · outbound

This paper cites Greedy function approximation: a gradient boost- ing machine,.

Guided Random Forest and its application to data approximation Greedy function approximation: a gradient boost- ing machine,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:51:53.845678Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 7b7c342d-1deb-437c-b880-0daea2a0cb6b · outbound

This paper cites Boosting the margin: A new explanation for the effectiveness of voting methods,.

Guided Random Forest and its application to data approximation Boosting the margin: A new explanation for the effectiveness of voting methods,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:51:53.829705Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 52a252e7-12cb-4bc8-91f4-94757c96c9e0 · outbound

This paper cites A decision-theoretic generalization of on-line learning and an application to boosting,.

Guided Random Forest and its application to data approximation A decision-theoretic generalization of on-line learning and an application to boosting,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:51:53.814071Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c4b30d87-594b-4cf8-8b6b-7ed951eded48 · outbound

This paper cites Bias, variance, and arcing classiﬁers,.

Guided Random Forest and its application to data approximation Bias, variance, and arcing classiﬁers,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:51:53.796399Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 2af1db85-8c90-4500-8483-af65ea2cd76b · outbound

This paper cites Bias plus variance decomposition for zero-one loss functions,.

Guided Random Forest and its application to data approximation Bias plus variance decomposition for zero-one loss functions,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:51:53.779730Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation cc4028bd-c0b9-4487-b925-b875e16503ad · outbound

This paper cites A uniﬁed bias-variance decomposition,.

Guided Random Forest and its application to data approximation A uniﬁed bias-variance decomposition,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:51:53.763235Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5f62c7b6-5f51-4225-97dd-83c79458cdac · outbound

This paper cites Variance and bias for general loss functions,.

Guided Random Forest and its application to data approximation Variance and bias for general loss functions,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:51:53.745764Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 4b1be3e7-19ac-45ee-8b5b-e894973b0f21 · outbound

This paper cites Weka: Practical machine learning tools and techniques with java implementations,.

Guided Random Forest and its application to data approximation Weka: Practical machine learning tools and techniques with java implementations,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:51:53.729510Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 4665b9e1-11d3-4529-ae0b-5ecc2f4770e6 · outbound

This paper cites The random subspace method for constructing decision forests,.

Guided Random Forest and its application to data approximation The random subspace method for constructing decision forests,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:51:53.712633Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 77db466f-c8b2-42af-91e0-c85338c60e24 · outbound

This paper cites Shape quantization and recognition with randomized trees,.

Guided Random Forest and its application to data approximation Shape quantization and recognition with randomized trees,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:51:53.696499Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ef1e291d-fcae-4a62-a874-7ee3c4dfacc7 · outbound

This paper cites UCI machine learning repository,.

Guided Random Forest and its application to data approximation UCI machine learning repository,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:51:53.678536Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Pith citing papers

Observation 0f50291d-38f5-4e70-9750-13d5f7a2e6c9 · inbound

Learning ON Large Datasets Using Bit-String Trees cites this paper.

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

Reference 84

Resolution: verified exact; local_arxiv, observed 2026-08-15T17:14:40.221309Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.