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ward_tree — scikit-learn 1.7.2 documentation
\sqrt{\frac{|v|+|s|} {T}d(v,s)^2 + \frac{|v|+|t|} {T}d(v,t)^2 - \frac{|v|} {T}d(s,t)^2}\]...\(d(u,v)\) is computed as follows, \[d(u,v) = \sqrt{\frac{|v|+|s|}...scikit-learn.org/stable/modules/generated/sklearn.cluster.ward_tree.html -
1.8. Cross decomposition — scikit-learn 1.7.2 d...
using the rotation matrix \(V(\Delta^T V)^{-1}\) , accessed via the...to compute \(u_k\) and \(v_k\) , \(v_k\) is never normalized....scikit-learn.org/stable/modules/cross_decomposition.html -
2.3. Clustering — scikit-learn 1.7.2 documentation
Likewise for \(V\) : \[H(V) = - \sum_{j=1}^{|V|}P'(j)\log(P'(j))\]...\frac{|U_i \cap V_j|}{N}\log\left(\frac{N|U_i \cap V_j|}{|U_i||V_j|}\right)\]...scikit-learn.org/stable/modules/clustering.html -
GOLF me！（ゴルフミー！）- 月額定額でゴルフが楽しめるサブスクリプションサービス
v=20251004094613" width="390" height="200"...ritanomori-cc-780x400-01.jpg?v=20251004094614" width="390" height="200"...golfme.goo.ne.jp/ -
GMM covariances — scikit-learn 1.7.2 documentation
degrees v = 2.0 * np . sqrt ( 2.0 ) * np . sqrt ( v ) ell = mpl...gmm . means_ [ n , : 2 ], v [ 0 ], v [ 1 ], angle = 180 + angle...scikit-learn.org/stable/auto_examples/mixture/plot_gmm_covariances.html -
maihamadb schema
(Out) V_IN_CHAR - CHAR (In) V_OUT_VARCHAR - VARCHAR2 (Out) V_INOUT_VARCHAR...(InOut) V_IN_NUMBER - NUMBER(22) (In) V_IN_DATE - DATE (In) V_IN_TIMESTAMP...dbflute.seasar.org/ja/data/doc/oracle/schema-maihamadb.html -
vendor.c366ef2db8dbebbba96c.js
v=0;for(v=0;v<256;v++)d[v]=v<128?v<<1:v<<1^283;for(v=0;v<256;v++){var...on=v,i.addListener=v,i.once=v,i.off=v,i.removeListener=v,i.r...www.buzzfeed.com/static-assets/js/vendor.c366ef2db8dbebbba96c.js -
mutual_info_score — scikit-learn 1.7.2 document...
\sum_{j=1}^{|V|} \frac{|U_i\cap V_j|}{N} \log\frac{N|U_i \cap V_j|}{|U_i||V_j|}\]...and \(|V_j|\) is the number of the samples in cluster \(V_j\) ,...scikit-learn.org/stable/modules/generated/sklearn.metrics.mutual_info_score.html -
2.9. Neural network models (unsupervised) — sci...
\[\begin{split}h_i \bot h_j | \mathbf{v} \\ v_i \bot v_j | \mathbf{h}\end{split}\]...\[E(\mathbf{v}, \mathbf{h}) = -\sum_i \sum_j w_{ij}v_ih_j - \sum_i...scikit-learn.org/stable/modules/neural_networks_unsupervised.html -
1.4. Support Vector Machines — scikit-learn 1.7...
support vectors \(v^{0}_1, v^{1}_1\) and \(v^{0}_2, v^{1}_2\) respectively....three support vectors \(v^{0}_0, v^{1}_0, v^{2}_0\) and class 1 and...scikit-learn.org/stable/modules/svm.html