On the public citation graph

Learning Distributions by Their Density Levels: A Paradigm for Learning without a Teacher

https://doi.org/10.1006/jcss.1997.1507

0 signed · 72 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.

Claim with ORCID

Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.

See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1006/jcss.1996.0019openalex-snapshot

General Bounds on the Number of Examples Needed for Learning Probabilistic Concepts

inferredcites →https://doi.org/10.1007/978-3-319-21852-6_3openalex-snapshot

On the Uniform Convergence of Relative Frequencies of Events to Their Probabilities

inferredcites →https://doi.org/10.1007/bf00116829openalex-snapshot

Learning From Noisy Examples

inferredcites →https://doi.org/10.1007/bf00993408openalex-snapshot

Bounding the Vapnik-Chervonenkis dimension of concept classes parameterized by real numbers

inferredcites →https://doi.org/10.1007/bfb0099432openalex-snapshot

A course on empirical processes

inferredcites →https://doi.org/10.1016/0020-0190(94)00171-topenalex-snapshot

Lower bounds for sampling algorithms for estimating the average

inferredcites →https://doi.org/10.1016/0304-3975(91)90026-xopenalex-snapshot

Learnability with respect to fixed distributions

inferredcites →https://doi.org/10.1016/0890-5401(89)90002-3openalex-snapshot

A general lower bound on the number of examples needed for learning

inferredcites →https://doi.org/10.1016/b978-1-55860-213-7.50009-2openalex-snapshot

Learning by Smoothing: a morphological approach

inferredcites →https://doi.org/10.1016/s0022-0000(05)80062-5openalex-snapshot

Efficient distribution-free learning of probabilistic concepts

inferredcites →https://doi.org/10.1016/s0734-189x(88)80033-1openalex-snapshot

A survey of the hough transform

inferredcites →https://doi.org/10.1109/fscs.1990.89557openalex-snapshot

Efficient distribution-free learning of probabilistic concepts

inferredcites →https://doi.org/10.1109/tac.1974.1100577openalex-snapshot

Pattern classification and scene analysis

inferredcites →https://doi.org/10.1109/tassp.1985.1164715openalex-snapshot

Probability, random variables and stochastic processes

inferredcites →https://doi.org/10.1137/0220021openalex-snapshot

Probably Approximate Learning of Sets and Functions

inferredcites →https://doi.org/10.1137/1116025openalex-snapshot

On the Uniform Convergence of Relative Frequencies of Events to Their Probabilities

inferredcites →https://doi.org/10.1145/168304.168364openalex-snapshot

Localization vs. identification of semi-algebraic sets

inferredcites →https://doi.org/10.1145/168304.168377openalex-snapshot

Bounding the Vapnik-Chervonenkis dimension of concept classes parameterized by real numbers

inferredcites →https://doi.org/10.1145/168304.168385openalex-snapshot

General bounds on the number of examples needed for learning probabilistic concepts

inferredcites →https://doi.org/10.1145/195058.195155openalex-snapshot

On the learnability of discrete distributions

inferredcites →https://doi.org/10.1145/225058.225118openalex-snapshot

Polynomial bounds for VC dimension of sigmoidal neural networks

inferredcites →https://doi.org/10.1145/76359.76371openalex-snapshot

Learnability and the Vapnik-Chervonenkis dimension

inferredcites →https://doi.org/10.2307/2288754openalex-snapshot

Probability, Random Variables, and Stochastic Processes.

inferredcites →https://doi.org/10.5555/114836.114842openalex-snapshot

Learning by smoothing: a morphological approach

inferredcites →https://doi.org/10.5555/93025.93068openalex-snapshot

A general lower bound on the number of examples needed for learning

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1002/jcaf.70010openalex-snapshot

The Role of Total Assets in Financial Performance: Evaluating Econometrics and Machine Learning Approaches

inferred← citeshttps://doi.org/10.1006/jcss.2001.1797openalex-snapshot

The Computational Complexity of Densest Region Detection

inferred← citeshttps://doi.org/10.1007/11539117_91openalex-snapshot

An Application of Support Vector Machines for Customer Churn Analysis: Credit Card Case

inferred← citeshttps://doi.org/10.1007/3-540-49730-7_9openalex-snapshot

PAC Learning from Positive Statistical Queries

inferred← citeshttps://doi.org/10.1007/978-3-319-90092-6_14openalex-snapshot

Recommending Based on Implicit Feedback

inferred← citeshttps://doi.org/10.1007/978-3-540-30463-0_27openalex-snapshot

One-Class Support Vector Machines and Density Estimation: The Precise Relation

inferred← citeshttps://doi.org/10.1007/978-3-642-16108-7_17openalex-snapshot

A Lower Bound for Learning Distributions Generated by Probabilistic Automata

inferred← citeshttps://doi.org/10.1016/j.asoc.2014.01.031openalex-snapshot

Churn prediction using comprehensible support vector machine: An analytical CRM application

inferred← citeshttps://doi.org/10.1016/j.ejor.2009.03.036openalex-snapshot

Support vector machines for default prediction of SMEs based on technology credit

inferred← citeshttps://doi.org/10.1016/j.eswa.2004.08.009openalex-snapshot

An application of support vector machines in bankruptcy prediction model

inferred← citeshttps://doi.org/10.1016/j.jssc.2024.124669openalex-snapshot

Novelty detection in the design of synthesis of garnet-structured solid electrolytes

inferred← citeshttps://doi.org/10.1016/j.knosys.2010.07.009openalex-snapshot

The use of hybrid manifold learning and support vector machines in the prediction of business failure

inferred← citeshttps://doi.org/10.1016/j.knosys.2010.10.003openalex-snapshot

A support vector machine-based model for detecting top management fraud

inferred← citeshttps://doi.org/10.1016/j.neucom.2011.10.032openalex-snapshot

Learning rates of support vector machine classifier for density level detection

inferred← citeshttps://doi.org/10.1016/j.neuron.2006.05.014openalex-snapshot

Orexin Neurons and the TASK of Glucosensing

inferred← citeshttps://doi.org/10.1016/s0304-3975(01)00265-1openalex-snapshot

PAC learning of probability distributions over a discrete domain

inferred← citeshttps://doi.org/10.1017/cbo9781139176224openalex-snapshot

Kernel Methods and Machine Learning

inferred← citeshttps://doi.org/10.1023/a:1008314532315openalex-snapshot

VC-Dimension Analysis of Object Recognition Tasks

inferred← citeshttps://doi.org/10.1109/aici.2009.450openalex-snapshot

A Novel Network Intrusion Detection Algorithm Based on Density Estimation

inferred← citeshttps://doi.org/10.1109/icdm.2008.16openalex-snapshot

One-Class Collaborative Filtering

inferred← citeshttps://doi.org/10.1109/icicta.2009.324openalex-snapshot

An Unsupervised Network Intrusion Detection Based on Anomaly Analysis

inferred← citeshttps://doi.org/10.1109/icme.2015.7177520openalex-snapshot

Joint learning for image-based handbag recommendation

inferred← citeshttps://doi.org/10.1109/jproc.2021.3052449openalex-snapshot

A Unifying Review of Deep and Shallow Anomaly Detection

inferred← citeshttps://doi.org/10.1109/nas.2008.41openalex-snapshot

Intrusion Detection Based on Density Level Sets Estimation

inferred← citeshttps://doi.org/10.1109/ssiai.2008.4512311openalex-snapshot

Subpixel Anomalous Change Detection in Remote Sensing Imagery

inferred← citeshttps://doi.org/10.1109/whispers.2011.6080893openalex-snapshot

Ellipsoid-simplex hybrid for hyperspectral anomaly detection

inferred← citeshttps://doi.org/10.1117/12.487069openalex-snapshot

Resampling approach for anomaly detection in multispectral images

inferred← citeshttps://doi.org/10.1117/12.883326openalex-snapshot

Overlapping image segmentation for context-dependent anomaly detection

inferred← citeshttps://doi.org/10.1142/s0219530505000625openalex-snapshot

LEARNING RATES FOR DENSITY LEVEL DETECTION

inferred← citeshttps://doi.org/10.1145/1150402.1150459openalex-snapshot

Outlier detection by active learning

inferred← citeshttps://doi.org/10.1145/1150402.1150501openalex-snapshot

Outlier detection by sampling with accuracy guarantees

inferred← citeshttps://doi.org/10.1145/238061.238082openalex-snapshot

The importance of convexity in learning with squared loss

inferred← citeshttps://doi.org/10.1162/089976601750264965openalex-snapshot

Estimating the Support of a High-Dimensional Distribution

inferred← citeshttps://doi.org/10.1214/15-aos1331openalex-snapshot

Fully adaptive density-based clustering

inferred← citeshttps://doi.org/10.26434/chemrxiv-2024-cr0q8openalex-snapshot

Novelty Detection in the Design of Synthesis of Energy Storage Materials: a Case Study of Garnet-Structured Solid Electrolytes

inferred← citeshttps://doi.org/10.32628/cseit172155openalex-snapshot

An Optimal Churn Prediction Model using Support Vector Machine with Adaboost

inferred← citeshttps://doi.org/10.32657/10356/69405openalex-snapshot

On handbag recognition and recommendation

inferred← citeshttps://doi.org/10.3741/jkwra.2014.47.11.1095openalex-snapshot

Spatial Downscaling of Grid Precipitation Using Support Vector Machine Regression

inferred← citeshttps://doi.org/10.4156/aiss.vol4.issue1.22openalex-snapshot

A New Network Intrusion Detection Algorithm based on Radial Basis Function Neural Networks Classifier

inferred← citeshttps://doi.org/10.4156/aiss.vol4.issue2.35openalex-snapshot

Study on Genetic Algorithm Optimization for Support Vector Machine in Network Intrusion Detection

inferred← citeshttps://doi.org/10.4156/jcit.vol7.issue11.2openalex-snapshot

Research on the Target Tracking Algorithm for Wireless Sensor Network Based on Improved Particle Filter

inferred← citeshttps://doi.org/10.4156/jcit.vol7.issue11.28openalex-snapshot

Study on the network attack prediction algorithm based on chaos theory and LSSVM

inferred← citeshttps://doi.org/10.48550/arxiv.0805.2891openalex-snapshot

Learning Low-Density Separators

inferred← citeshttps://doi.org/10.5539/mas.v3n12p0openalex-snapshot

Modern Applied Science, Vol. 3, No. 12, December, 2009

inferred← citeshttps://doi.org/10.5539/mas.v3n12p28openalex-snapshot

Prediction of Stock Market Index Movement by Ten Data Mining Techniques

inferred← citeshttps://doi.org/10.7282/t3h993gsopenalex-snapshot

Stochastic dilemmas

inferred← citeshttps://doi.org/10.9790/0661-16518894openalex-snapshot

Performance Comparison of SVM and ANN in Predicting Compressive Strength of Concrete

Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.