... images **from** 130 clips (chunklets) of 20 different ...chunklets **from** a Markovian process tends to provide chunklets with dependent data points, which supply less information regarding the within class ...

... see **from** Table 4, DML-eig consistently improves k-NN classification using Euclidean distance on most data ...Hence, **learning** a **Mahalanobis** **metric** **from** training data does lead to ...

... ground **metric** given a training set of labeled ...and **metric** **learning** techniques in Section 5, in particular **Mahalanobis** **metric** **learning** techniques (Xing et ...the **metric** ...

... As reported in Table 1, we can conclude: 1) B OOST M ETRIC consistently improves the accu- racy of kNN classification using Euclidean distance on most data sets. So **learning** a **Mahalanobis** **metric** ...

... **Metric** **learning** using convex optimization has attracted a lot of attention recently ...a **Mahalanobis** **metric** for clustering using convex optimization to minimize the distance between examples ...

... of **learning** a linear transformation of the input data and applied it to the problems of **metric** and kernel **learning**, with a focus on establishing connec- tions between the two ...for **learning** a ...

... distance **constraints** are randomly gen- erated **from** the original kernel matrix with α = ...directly **from** distance ...start **from** the identity matrix that do not encode any domain ...distance ...

... data **learning** such as internet ...Tree **Learning** (IDTL) that use the principle through Incremental Linear Discriminant Analysis (ILDA) together with **Mahalanobis** distance for classification of the ...

... components **from** the vegetated area is recognized by utilizing the least square fitting ...area **from** the partially vegetated (wet and dry) regions then identify by a line parallel to soil ...

... for **learning** multiplicity word and tree automata (Bailly et ...spectral **learning** algorithm (based on singular value decomposition) for hidden Markov ...passive **learning** framework in which one is ...

... 3D-space **metric** increases as was proved in ...whole **metric** equals the change rate of spatial part of the ...spatial **metric** with = 1,2,3 into FRW equation which reveals a relation between spatial ...

... cone **metric** space (X , d) is solid and normal, most of the fixed point problems can be reduced to their standard **metric** ...the **metric** D(x, y) = kd(x, y)k (see details in ...

... Eisenhart (1927) gave the theory of conformal structures arose in studying those properties of Riemannian and pseudo-Riemannian manifolds that remain invariant under conformal transformations of the **metric**. The ...

... omitted. **From** the remain- ing 771 LDV words, there were 231 words that had five or more synonyms in the combined ...set **from** four partitions as follows; for each query word in the partitions, we randomly ...

... effect **from** observational data requires strong ...possible **equivalence** classes of ...models **from** finite data, we investigate how to strengthen assumptions in order to make the statistical problem ...

... automatically **from** the time domain ...borrowed **from** statistics and machine **learning**, the most commonly used being control charts [6], outlier analysis using the **Mahalanobis** squared-distance ...

... two middle consonants are adjacent, while the two edge consonants are not. However, participants did not need to learn any relation among consonants at all; rather, they just had to remember the positions in which each ...

... Table 2 shows that the use of FUMES to retrieve updated learner models adds just over one second to the initialization time of the APeLS-based SQL web course. This should not impact greatly on the usability of the ...

... inferences **from** tests of measurement or structural invariance, we examined four different statisti- cal criteria: (a) the unadjusted p-value associated with the likelihood-ratio test; (b) the ...

... reinforcement **learning**, the guidance algorithm parameters are optimized in a stochastic ...the **learning** process, the resulting parameters will not only provide optimality **from** a fuel consumption ...