Paper Citation Record · LEDGER

Dynamic Logistic Ensembles with Recursive Probability and Automatic Subset Splitting for Enhanced Binary Classification

As of 13 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 0 inbound Pith citation observations for arXiv:2411.18649.

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

pith.paper-citation-record.v1
2411.18649 v1

Coverage vector

measured 19 of 19 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T11:43:56.802162Z

measured 19 of 19 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

19 of 19 outbound references displayed

verified exact0
verified fuzzy7
unresolved10
parse uncertain0
malformed identifier2
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 033af203-130b-4134-b585-5bd24386471c · outbound

This paper cites Hosmer and S.

Dynamic Logistic Ensembles with Recursive Probability and Automatic Subset Splitting for Enhanced Binary Classification Hosmer and S

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-12T11:43:56.719318Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 11f8b10b-e82d-4ea1-91ee-a52a7a596584 · outbound

This paper cites The Regression Analysis of Binary Sequences,.

Dynamic Logistic Ensembles with Recursive Probability and Automatic Subset Splitting for Enhanced Binary Classification The Regression Analysis of Binary Sequences,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-12T11:43:56.723912Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8577a449-2272-4c7a-9a85-77e73c05cc9c · outbound

This paper cites Bagging predictors,.

Dynamic Logistic Ensembles with Recursive Probability and Automatic Subset Splitting for Enhanced Binary Classification Bagging predictors,

Reference 3

Resolution: malformed identifier; no resolver link, observed 2026-08-12T11:43:56.727857Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2e39dd57-9cc6-4b56-bc5c-d139a7ed8b8a · outbound

This paper cites A Decision Theoretic Generalization of On-Line Learning and an Application to Boosting,.

Dynamic Logistic Ensembles with Recursive Probability and Automatic Subset Splitting for Enhanced Binary Classification A Decision Theoretic Generalization of On-Line Learning and an Application to Boosting,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:43:57.311520Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f2019d64-e447-4b3a-a910-788624e2793b · outbound

This paper cites The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery.

Dynamic Logistic Ensembles with Recursive Probability and Automatic Subset Splitting for Enhanced Binary Classification The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-12T11:43:56.735706Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ad835d17-ab3c-42cb-9cad-5d278587dde7 · outbound

This paper cites Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead,.

Dynamic Logistic Ensembles with Recursive Probability and Automatic Subset Splitting for Enhanced Binary Classification Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-12T11:43:56.740069Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1b34df79-890a-4e18-9b59-79ca0e994c79 · outbound

This paper cites Towards A Rigorous Science of Interpretable Machine Learning.

Dynamic Logistic Ensembles with Recursive Probability and Automatic Subset Splitting for Enhanced Binary Classification Towards A Rigorous Science of Interpretable Machine Learning

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-12T11:43:56.745081Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6e54ab5a-cbc9-4c21-ab5b-062c0490418b · outbound

This paper cites A brief introduction to boosting,.

Dynamic Logistic Ensembles with Recursive Probability and Automatic Subset Splitting for Enhanced Binary Classification A brief introduction to boosting,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:43:57.285319Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 3f4c2c24-403b-41be-966d-ec80b47d409a · outbound

This paper cites The mythos of model interpretability,.

Dynamic Logistic Ensembles with Recursive Probability and Automatic Subset Splitting for Enhanced Binary Classification The mythos of model interpretability,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:43:57.272194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 5018359a-da72-4ee2-b32a-6a989786c04c · outbound

This paper cites Random forests,.

Dynamic Logistic Ensembles with Recursive Probability and Automatic Subset Splitting for Enhanced Binary Classification Random forests,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-12T11:43:56.765794Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ee72dc30-59f3-41e5-84fe-93b93688d0b6 · outbound

This paper cites Greedy function approximation: A gradient boosting machine.

Dynamic Logistic Ensembles with Recursive Probability and Automatic Subset Splitting for Enhanced Binary Classification Greedy function approximation: A gradient boosting machine

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:43:57.298453Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 224e50d5-2058-45d8-9654-0acff124e0cb · outbound

This paper cites Application of the logistic function to bio-assay,.

Dynamic Logistic Ensembles with Recursive Probability and Automatic Subset Splitting for Enhanced Binary Classification Application of the logistic function to bio-assay,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-12T11:43:56.777846Z

Source-reported events for the cited work

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Observation de53cae2-d367-4cce-9c85-162447d6dc32 · outbound

This paper cites Available: https://doi.org/10.1214/aos/1013203451.

Dynamic Logistic Ensembles with Recursive Probability and Automatic Subset Splitting for Enhanced Binary Classification Available: https://doi.org/10.1214/aos/1013203451

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-12T11:43:56.773981Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4f64d948-8f6e-499b-9f9b-90b5fa7a5206 · outbound

This paper cites Logistic regression for data mining and high-dimensional classification,.

Dynamic Logistic Ensembles with Recursive Probability and Automatic Subset Splitting for Enhanced Binary Classification Logistic regression for data mining and high-dimensional classification,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:43:57.249787Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 991bcc11-5703-4bee-97ee-1da4deeb1bca · outbound

This paper cites an unresolved cited work.

Dynamic Logistic Ensembles with Recursive Probability and Automatic Subset Splitting for Enhanced Binary Classification Unresolved cited work

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-12T11:43:56.781955Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e712a425-6e7e-40b5-bce0-49488f87dbae · outbound

This paper cites Backpropagation through time: what it does and how to do it,.

Dynamic Logistic Ensembles with Recursive Probability and Automatic Subset Splitting for Enhanced Binary Classification Backpropagation through time: what it does and how to do it,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:43:57.222638Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 3766f6b9-3d3f-465d-86f1-50c926686582 · outbound

This paper cites Ensemble methods in machine learning,.

Dynamic Logistic Ensembles with Recursive Probability and Automatic Subset Splitting for Enhanced Binary Classification Ensemble methods in machine learning,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:43:57.236048Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8a18da89-af67-4945-984f-60c7fe5d2fa6 · outbound

This paper cites Learning Complex, Extended Sequences Using the Principle of History Compression,.

Dynamic Logistic Ensembles with Recursive Probability and Automatic Subset Splitting for Enhanced Binary Classification Learning Complex, Extended Sequences Using the Principle of History Compression,

Reference 19

Resolution: malformed identifier; raw_fallback, observed 2026-08-12T11:43:57.200084Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation de8862b2-22ce-4c3e-b4dc-c525f392dc90 · outbound

This paper cites Induction of decision trees,.

Dynamic Logistic Ensembles with Recursive Probability and Automatic Subset Splitting for Enhanced Binary Classification Induction of decision trees,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-12T11:43:56.798083Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.