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Learning a Mahalanobis Metric from Equivalence Constraints
... 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 ...
Distance Metric Learning with Eigenvalue Optimization
... 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 Learning
... 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 ...
Positive Semidefinite Metric Learning Using Boosting-like Algorithms
... 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 with convex optimization
... 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 ...
Metric and Kernel Learning Using a Linear Transformation
... 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 ...
Regression on Fixed-Rank Positive Semidefinite Matrices: A Riemannian Approach
... 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 ...
A New Incremental Decision Tree Learning for Cyber Security based on ILDA and Mahalanobis 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 ...
New Fully Automatic Multispectral Image Classification based on Scatterplot Method
... 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 ...
Complexity of Equivalence and Learning for Multiplicity Tree Automata
... 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 ...
On Equivalence of Quantum Liouville Equation and Metric Compatibility Condition, a Ricci Flow Approach
... 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 ...
Vol 2013
... 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 ...
Review of complex conformal structures of 1 dimensional Riemannian manifold
... 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 ...
Metric Learning for Synonym Acquisition
... 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 ...
Learning Instrumental Variables with Structural and Non-Gaussianity Assumptions
... 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 ...
A comparison of linear approaches to filter out environmental effects in structural health monitoring
... 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 ...
Perceptual Constraints in Phonotactic Learning
... 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 ...
Supporting Learner Model Exchange in Educational Web Systems
... 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 ...
Measurement invariance of the kidney disease and quality of life instrument (KDQOL-SF) across Veterans and non-Veterans
... 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 ...
Terminal multiple surface sliding guidance for planetary landing : Development, tuning and optimization via reinforcement learning
... 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 ...