This is the first paper to analyze tripartite linguistic structure of Isaiah using Random Forest Regression, a supervised machine learning statistical approach.  By predicting occurrences ‘judgment’ and ‘hope’ verses, we examine threefold (section 1--chapters 1-39; section 2--chapters 40-55; 3--chapters 56-66) for differences in expression within...

Memprediksi laju penguapan memiliki manfaat yang luas dalam berbagai aplikasi seperti manajemen sumber daya air, pertanian, dan lingkungan hidup. Namun untuk mendapatkan data lengkap akurat mempelajari tantangan tersendiri. Selain itu, rendahnya tingkat linieritas antara faktor meteorologi lainnya di wilayah tropis dapat menyebabkan hasil prediksi bervariasi. Tujuan dari penelitian ini adalah m...

FORWARD ALGORITHMS Two observations: • PAVA is efficient and generalizes to partial orders • Follow The Leader algorithms are common in practice Forward Algorithm: To predict at xt, imagine y′ t ∈ [0, 1], compute f∗ on {(x1, y1) . . . (xt−1, yt−1)} ∪ {(xt, y′ t)}, and play ŷt = f(xt). FORWARD ALGORITHM EXAMPLES • IR-Int: Compute f∗ on past data. Predict with average of f∗ at nearest xi. • Inter...

We investigate a method for regression that makes use of a randomly generated subspace GP ⊂ F (of finite dimension P) of a given large (possibly infinite) dimensional function space F , for example, L2([0,1] d ;R). GP is defined as the span of P random features that are linear combinations of a basis functions of F weighted by random Gaussian i.i.d. coefficients. We show practical motivation fo...

This article deals with random projections applied as a data reduction technique for Bayesian regression analysis. We show sufficient conditions under which the entire d-dimensional distribution is approximately preserved under random projections by reducing the number of data points from n to k ∈ O(poly(d/ε)) in the case n d. Under mild assumptions, we prove that evaluating a Gaussian likeliho...

In this work we present a novel approach to ensemble learning for regression models, by combining the ensemble generation technique of random subspace method with the ensemble integration methods of Stacked Regression and Dynamic Selection. We show that for simple regression methods such as global linear regression and nearest neighbours, this is a more effective method than the popular ensembl...