• RBoost

  • Referenced in 3 articles [sw29975]
  • robust to the noisy data compared with AdaBoost. RBoost1 and RBoost2 optimize a nonconvex loss ... robustness of the proposed algorithms to the noisy training and testing samples. Experimental results...
  • ACOSampling

  • Referenced in 5 articles [sw41786]
  • based on the idea of ant colony optimization (ACO) to address this problem. The algorithm ... starts with feature selection technology to eliminate noisy genes in data. Then we randomly ... informative majority samples and search the corresponding optimal training sample subset. At last, the statistical...
  • convex_learning

  • Referenced in 4 articles [sw32735]
  • convexity, globally optimal solutions are further computed numerically for applications with incomplete, noisy and blurry...
  • BayesianOptimization.jl

  • Referenced in 1 article [sw42275]
  • Bayesian optimization is a global optimization strategy for (potentially noisy) functions with unknown derivatives. With ... alternatives, making it well suited for the optimization of costly objective functions. Well known examples...
  • PSOFuzzySVM-TMH

  • Referenced in 3 articles [sw27664]
  • protein sequences. The noisy and extraneous attributes are eradicated using an optimization selection technique, particle...
  • NARROMI

  • Referenced in 6 articles [sw24066]
  • optimization (RO) and information theory-based mutual information (MI). In the proposed algorithm, the noisy...
  • TVR-DART

  • Referenced in 23 articles [sw27583]
  • solves the discrete reconstruction problem within an optimization framework inspired by compressive sensing to steer ... more accurate reconstruction than existing algorithms under noisy conditions from a small number of projection...
  • FEYNMAN

  • Referenced in 5 articles [sw06056]
  • noisy environments. The investigation of these and related questions often requires a search or optimization...
  • Cirq

  • Referenced in 7 articles [sw34753]
  • software library for writing, manipulating, and optimizing quantum circuits and then running them against quantum ... abstracting them away, because, in the Noisy Intermediate-Scale Quantum (NISQ) regime, these details determine...
  • EKF/UKF Toolbox

  • Referenced in 4 articles [sw21920]
  • optimal filtering toolbox for Matlab. Optimal filtering is a frequently used term for a process ... dynamic system is estimated through noisy and indirect measurements. This toolbox mainly consists of Kalman ... toolbox is not to provide highly optimized software package, but instead to provide a simple...
  • ZOOpt

  • Referenced in 2 articles [sw22396]
  • particularly focuses on optimization problems in machine learning, addressing high-dimensional, noisy, and large-scale...
  • MOUGH

  • Referenced in 2 articles [sw36675]
  • transform is proposed, voting using GMMs and optimized via expectation-maximization that is capable ... based on a training dataset of (possibly noisy) images with only crude estimates of scale ... each image. Further modifications are proposed to optimize the algorithm for tracking. The method...
  • Deep Speech

  • Referenced in 6 articles [sw40429]
  • tend to perform poorly when used in noisy environments. In contrast, our system does ... approach is a well-optimized RNN training system that uses multiple GPUs, as well ... test set. Deep Speech also handles challenging noisy environments better than widely used, state...
  • FacetNet

  • Referenced in 16 articles [sw20426]
  • this approach is inappropriate in applications with noisy data. In this paper, we propose FacetNet ... which is guaranteed to converge to an optimal solution. We perform extensive experimental studies...
  • IDRLnet

  • Referenced in 1 article [sw39547]
  • metrics, and optimizers within Python. Furthermore, it provides functionality to solve noisy inverse problems, variational...
  • krisp

  • Referenced in 1 article [sw14717]
  • also known as Gaussian process regression) and optimization of deterministic simulators. The toolbox consists ... creation of kriging model for deterministic or noisy data (correlation kernels, hyper-parameter estimation, prediction ... validation). 2. methods implementing Expected Improvement based optimization for time consuming deterministic functions...
  • FPDclustering

  • Referenced in 3 articles [sw15462]
  • used with non-spherical clusters, outliers, or noisy data. Facto PD-clustering (FPDC ... linear transformation of variables and a cluster optimizing the PD-clustering criterion. It works...
  • Py-BOBYQA

  • Referenced in 1 article [sw32721]
  • BOBYQA: Derivative-Free Optimizer for Bound-Constrained Minimization. Py-BOBYQA is a flexible package ... objective function are expensive and/or noisy...
  • SINDy-PI

  • Referenced in 4 articles [sw40367]
  • nonlinearities. The SINDy-PI framework includes multiple optimization algorithms and a principled approach to model ... equations and conservation laws from limited and noisy data. In particular, we show that...
  • trajectory

  • Referenced in 1 article [sw22650]
  • circular motion under the conditions of incomplete noisy measurements is formulated and implemented ... shown how to apply algorithms for discrete optimal filtering to the evaluation of such...