Global convergence of Negative Correlation Extreme Learning Machine

• URL: http://arxiv.org/abs/2009.14695v2
• Date: Tue, 30 Mar 2021 09:18:57 GMT
• Title: Global convergence of Negative Correlation Extreme Learning Machine
• Abstract summary: This paper mathematically presents the sufficient conditions to guarantee the global convergence of NCELM. The update of the ensemble in each iteration is defined as a contraction mapping function, and through Banach theorem, global convergence of the ensemble is proved.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Ensemble approaches introduced in the Extreme Learning Machine (ELM) literature mainly come from methods that relies on data sampling procedures, under the assumption that the training data are heterogeneously enough to set up diverse base learners. To overcome this assumption, it was proposed an ELM ensemble method based on the Negative Correlation Learning (NCL) framework, called Negative Correlation Extreme Learning Machine (NCELM). This model works in two stages: i) different ELMs are generated as base learners with random weights in the hidden layer, and ii) a NCL penalty term with the information of the ensemble prediction is introduced in each ELM minimization problem, updating the base learners, iii) second step is iterated until the ensemble converges. Although this NCL ensemble method was validated by an experimental study with multiple benchmark datasets, no information was given on the conditions about this convergence. This paper mathematically presents the sufficient conditions to guarantee the global convergence of NCELM. The update of the ensemble in each iteration is defined as a contraction mapping function, and through Banach theorem, global convergence of the ensemble is proved.

Related papers

• Transfer Learning in Nonparametric Regression with Deep ReLU Networks [9.811354059045287]
  The paper develops a general transfer learning framework for nonparametric regression with data consisting of multiple groups.<n>Conditions that enable positive transfer with faster rates are considered, including learning with simpler functions and data augmentation through pooling samples across groups.<n>Various simulations and real-data experiments further validate the effectiveness of the proposed method.
  arXiv  Detail & Related papers  (2026-08-20T16:53:00Z)
• Variational Consensus Monte Carlo for Bayesian Mixture [0.0]
  Motivated by the privacy, sensitivity and sharing limitations of health data, we present a comprehensive pipeline for inference of Bayesian mixture models.<n>We adopt a Consensus Monte Carlo approach, in which an MCMC algorithm is run independently within each data silo to estimate local posterior distributions.<n>A comprehensive simulation study validates the framework and allows us to compare to state-of-the-art federated learning alternatives.
  arXiv  Detail & Related papers  (2026-06-17T22:48:43Z)
• Influence-Guided Symbolic Regression: Scientific Discovery via LLM-Driven Equation Search with Granular Feedback [56.69850045068714]
  We introduce textitInfluence-Guided Regression (IGSR), a method that frames equation discovery as an iterative two-step process.<n>We demonstrate IGSRs effectiveness on a diverse suite of benchmarks, including LLM-SRBench, pharmacological PKPD models, an epidemiological simulation, and real-world genomic data.
  arXiv  Detail & Related papers  (2026-05-27T23:48:01Z)
• Overspecified Mixture Discriminant Analysis: Exponential Convergence, Statistical Guarantees, and Remote Sensing Applications [2.124297073085513]
  This study explores the classification error of Mixture Discriminant Analysis (MDA) in scenarios where the number of mixture components exceeds those present in the actual data distribution.<n>We analyze both the algorithmic convergence of the Expectation-Maximization (EM) algorithm and the statistical classification error.
  arXiv  Detail & Related papers  (2025-10-30T23:56:56Z)
• Train with Perturbation, Infer after Merging: A Two-Stage Framework for Continual Learning [57.514786046966265]
  We propose textbfPerturb-and-Merge (P&M), a novel continual learning framework that integrates model merging into the CL paradigm to mitigate forgetting.<n>Our proposed approach achieves state-of-the-art performance on several continual learning benchmark datasets.
  arXiv  Detail & Related papers  (2025-05-28T14:14:19Z)
• Learning a Class of Mixed Linear Regressions: Global Convergence under General Data Conditions [1.9295130374196499]
  Mixed linear regression (MLR) has attracted increasing attention because of its great theoretical and practical importance in nonlinear relationships by utilizing a mixture of linear regression sub-models. Although considerable efforts have been devoted to the learning problem of such systems, most existing investigations impose the strict independent and identically distributed (i.i.d.) or distributed PE conditions.
  arXiv  Detail & Related papers  (2025-03-24T09:57:39Z)
• Federated Variational Inference for Bayesian Mixture Models [0.0]
  We introduce a principled 'divide and conquer' inference procedure using variational inference with local merge and delete moves. We show that these merge moves require only summaries of the data in each batch, enabling federated learning across local nodes. We validate the practical utility of the method by applying it to large scale electronic health record (EHR) data.
  arXiv  Detail & Related papers  (2025-02-18T09:41:23Z)
• Interpetable Target-Feature Aggregation for Multi-Task Learning based on Bias-Variance Analysis [53.38518232934096]
  Multi-task learning (MTL) is a powerful machine learning paradigm designed to leverage shared knowledge across tasks to improve generalization and performance. We propose an MTL approach at the intersection between task clustering and feature transformation based on a two-phase iterative aggregation of targets and features. In both phases, a key aspect is to preserve the interpretability of the reduced targets and features through the aggregation with the mean, which is motivated by applications to Earth science.
  arXiv  Detail & Related papers  (2024-06-12T08:30:16Z)
• Rethinking Clustered Federated Learning in NOMA Enhanced Wireless Networks [60.09912912343705]
  This study explores the benefits of integrating the novel clustered federated learning (CFL) approach with non-independent and identically distributed (non-IID) datasets. A detailed theoretical analysis of the generalization gap that measures the degree of non-IID in the data distribution is presented. Solutions to address the challenges posed by non-IID conditions are proposed with the analysis of the properties.
  arXiv  Detail & Related papers  (2024-03-05T17:49:09Z)
• Global Convergence of Online Identification for Mixed Linear Regression [1.9295130374196499]
  Mixed linear regression (MLR) is a powerful model for characterizing nonlinear relationships. This paper investigates the online identification and data clustering problems for two basic classes of MLRs. It introduces two corresponding new online identification algorithms based on the expectation-maximization principle.
  arXiv  Detail & Related papers  (2023-11-30T12:30:42Z)
• Generalized Oversampling for Learning from Imbalanced datasets and Associated Theory [0.0]
  In supervised learning, it is quite frequent to be confronted with real imbalanced datasets. We propose a data augmentation procedure, the GOLIATH algorithm, based on kernel density estimates. We evaluate the performance of the GOLIATH algorithm in imbalanced regression situations.
  arXiv  Detail & Related papers  (2023-08-05T23:08:08Z)
• Depersonalized Federated Learning: Tackling Statistical Heterogeneity by Alternating Stochastic Gradient Descent [6.394263208820851]
  Federated learning (FL) enables devices to train a common machine learning (ML) model for intelligent inference without data sharing. Raw data held by various cooperativelyicipators are always non-identically distributedly. We propose a new FL that can significantly statistical optimize by the de-speed of this process.
  arXiv  Detail & Related papers  (2022-10-07T10:30:39Z)
• Information-Theoretic Generalization Bounds for Iterative Semi-Supervised Learning [81.1071978288003]
  In particular, we seek to understand the behaviour of the em generalization error of iterative SSL algorithms using information-theoretic principles. Our theoretical results suggest that when the class conditional variances are not too large, the upper bound on the generalization error decreases monotonically with the number of iterations, but quickly saturates.
  arXiv  Detail & Related papers  (2021-10-03T05:38:49Z)
• A survey on Bayesian inference for Gaussian mixture model [3.109306676759862]
  The aim of this survey is to give a self-contained introduction to concepts and mathematical tools in Bayesian inference for finite and infinite Gaussian mixture model. Other than this modest background, the development is self-contained, with rigorous proofs provided throughout.
  arXiv  Detail & Related papers  (2021-08-20T13:23:17Z)
• Posterior-Aided Regularization for Likelihood-Free Inference [23.708122045184698]
  Posterior-Aided Regularization (PAR) is applicable to learning the density estimator, regardless of the model structure. We provide a unified estimation method of PAR to estimate both reverse KL term and mutual information term with a single neural network.
  arXiv  Detail & Related papers  (2021-02-15T16:59:30Z)
• Model Fusion with Kullback--Leibler Divergence [58.20269014662046]
  We propose a method to fuse posterior distributions learned from heterogeneous datasets. Our algorithm relies on a mean field assumption for both the fused model and the individual dataset posteriors.
  arXiv  Detail & Related papers  (2020-07-13T03:27:45Z)
• Good Classifiers are Abundant in the Interpolating Regime [64.72044662855612]
  We develop a methodology to compute precisely the full distribution of test errors among interpolating classifiers. We find that test errors tend to concentrate around a small typical value $varepsilon*$, which deviates substantially from the test error of worst-case interpolating model. Our results show that the usual style of analysis in statistical learning theory may not be fine-grained enough to capture the good generalization performance observed in practice.
  arXiv  Detail & Related papers  (2020-06-22T21:12:31Z)
• Theoretical Convergence of Multi-Step Model-Agnostic Meta-Learning [63.64636047748605]
  We develop a new theoretical framework to provide convergence guarantee for the general multi-step MAML algorithm. In particular, our results suggest that an inner-stage step needs to be chosen inversely proportional to $N$ of inner-stage steps in order for $N$ MAML to have guaranteed convergence.
  arXiv  Detail & Related papers  (2020-02-18T19:17:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.