local norm 地区常模 local optimum 局部最优 local pattern 局部模式 local phenomenon 局部现象 local population 地域人口 local reaction 局部反应 local reflex 局部反射 local response 局部反应 local restriction 局部制约 local sign 部位记号 local stability 局部稳定性 local terminal 本机终端 local vibration 局...
logical error 逻辑错误logical expression 逻辑表达logical formula 逻辑式logical grammar 逻辑语法logical ground 逻辑的根据logical idealism 逻辑唯心论logical identity 逻辑的同一logical inclusion 逻辑的包含logical interdependence 逻辑的相互依赖logical judgment 逻辑判断logical knowledge 逻辑认识logical language 逻辑语言...
2.1.1381 Part 1 Section 21.1.2.1.3, normAutofit (Normal AutoFit) 2.1.1382 Part 1 Section 21.1.2.1.4, spAutoFit (Shape AutoFit) 2.1.1383 Part 1 Section 21.1.2.2.2, defPPr (Default Paragraph Style) 2.1.1384 Part 1 Section 21.1.2.2.3, endParaRPr (End Paragraph Run Pro...
We show that the well-known trapezoidal rule is the unique optimal quadrature formula and determine its error norm explicitly.doi:10.1016/0021-9045(73)90071-3Wilhelm ForstElsevier Inc.journal of approximation theory
2.1.1302 Part 4 Section 5.1.5.1.3, normAutofit (Normal AutoFit) 2.1.1303 Part 4 Section 5.1.5.1.4, spAutoFit (Shape AutoFit) 2.1.1304 Part 4 Section 5.1.5.2.2, defPPr (Default Paragraph Style) 2.1.1305 Part 4 Section 5.1.5.2.3, endParaRPr (End Paragraph Run Properties...
where \(\varepsilon > 0\) should be set arbitrarily small, to closely make the \(LogSum\) penalty resemble the L0-norm. Equation (19) has a local minimal39. $$f_{Logsum} (w_{j} ,\lambda ,\varepsilon ) = D(w_{j} ,\lambda ,\varepsilon ) = \left\{ {\begin{array}{*{...
The L2loss operation computes the L2loss (based on the squared L2norm) given network predictions and target values. When theReductionoption is"sum"and theNormalizationFactoroption is"batch-size", the computed value is known as the mean squared error (MSE). ...
L2(Ω) denotes the space of all random variables X with E|X|2<∞, it is a Banach space with norm ∥X∥2=(E|X|2)1/2. L2[0,∞) is the space of square integrable functions over [0, ∞). The symbol ‘*’ within a matrix represents the symmetric terms of the matrix, e.g.(...
what we are doing is that instead of just minimizing the residual sum of squares we also have a penalty term on the β's. This penalty term is λ(a pre-chosen constant)times the squared norm of the β vector. This means that if the βj's take on large values, the optimization func...
A more practical type of discrepancy appears if the L∞-norm in (4) is replaced by the L2-norm: TN(B;P)=∫[0,1)sR(B;P)2du1/2. Again, depending on the family B, we can define several types of L2-discrepancy. Definition 3 The L2-star discrepancy TN*(P)=TN(J*;P), where...