Section Channel classification (Binary classification part): Weights obtained by the optimised classification algorithm. by Fabien Raphel (9421987)

An example of a type of texture of binary preferred/non-preferred ranges of raw vegetation index values type feature. by Elise Varaela Voltura (17592996)

Reproducing kernel-based semiparametric functional smoothed score estimation with binary responses by Meichen Liu (8168148)

Data_Sheet_1_Posiform planting: generating QUBO instances for benchmarking.pdf by Georg Hahn (12530469)

“…<p>We are interested in benchmarking both quantum annealing and classical algorithms for minimizing quadratic unconstrained binary optimization (QUBO) problems. …”

Data_Sheet_1_Pneumonia detection by binary classification: classical, quantum, and hybrid approaches for support vector machine (SVM).pdf by Sai Sakunthala Guddanti (17739363)

“…A support vector machine (SVM) is attractive because binary classification can be represented as an optimization problem, in particular as a Quadratic Unconstrained Binary Optimization (QUBO) model, which, in turn, maps naturally to an Ising model, thereby making annealing—classical, quantum, and hybrid—an attractive approach to explore. …”

<i>hi</i>PRS algorithm process flow. by Michela C. Massi (14599915)

“…<b>(C)</b> The whole training data is then scanned, searching for these sequences and deriving a re-encoded dataset where interaction terms are binary features (i.e., 1 if sequence <i>i</i> is observed in <i>j</i>-th patient genotype, 0 otherwise). …”

Schematic representation of semi-automated algorithm for organoids analyzing. by Anna Demchenko (18449439)

Supplementary Material for: Penalized Logistic Regression Analysis for Genetic Association Studies of Binary Phenotypes by Yu Y. (3096192)

“…We consider two approximate approaches to maximizing the marginal likelihood: (i) a Monte Carlo EM algorithm (MCEM) and (ii) a Laplace approximation (LA) to each integral, followed by derivative-free optimization of the approximation. …”

MCLP_quantum_annealer_V0.5 by Anonymous Anonymous (4854526)

“…Theoretical and applied experiments are conducted using four solvers: QBSolv, D-Wave Hybrid binary quadratic model 2, D-Wave Advantage system 4.1, and Gurobi. …”

Improved support vector machine classification algorithm based on adaptive feature weight updating in the Hadoop cluster environment by Jianfang Cao (1881379)

“…The MapReduce parallel programming model on the Hadoop platform is used to perform an adaptive fusion of hue, local binary pattern (LBP) and scale-invariant feature transform (SIFT) features extracted from images to derive optimal combinations of weights. …”

An example of a type of percent cover of preferred ranges of vegetation index texture values type feature. by Elise Varaela Voltura (17592996)

Latent state estimations for experimental data with no stressors. by Samiul Alam (18563323)

Latent state estimations for experimental data with interruptions for subject 18. by Samiul Alam (18563323)

Latent state estimations for experimental data with time limit. by Samiul Alam (18563323)

Latent state estimations for experimental data with no stressors for subject 22. by Samiul Alam (18563323)

Latent state estimations for experimental data with no stressors for subject 17. by Samiul Alam (18563323)

Latent state estimations for experimental data with interruptions for subject 13. by Samiul Alam (18563323)

Latent state estimations for experimental data with no stressors for subject 3. by Samiul Alam (18563323)

Latent state estimations for experimental data with time limit for subject 17. by Samiul Alam (18563323)

Latent state estimations for experimental data with no stressors for subject 15. by Samiul Alam (18563323)