[PDF][PDF] Learning a Mahalanobis metric from equivalence constraints.
Many learning algorithms use a metric defined over the input space as a principal tool, and
their performance critically depends on the quality of this metric. We address the problem of …
their performance critically depends on the quality of this metric. We address the problem of …
[PDF][PDF] Learning a Mahalanobis Metric from Equivalence Constraints
A Bar-Hillel, T Hertz, N Shental… - Journal of Machine …, 2005 - academia.edu
Many learning algorithms use a metric defined over the input space as a principal tool, and
their performance critically depends on the quality of this metric. We address the problem of …
their performance critically depends on the quality of this metric. We address the problem of …
[PDF][PDF] Learning a Mahalanobis Metric from Equivalence Constraints
A Bar-Hillel, T Hertz, N Shental… - Journal of Machine …, 2005 - researchgate.net
Many learning algorithms use a metric defined over the input space as a principal tool, and
their performance critically depends on the quality of this metric. We address the problem of …
their performance critically depends on the quality of this metric. We address the problem of …
[PDF][PDF] Learning a Mahalanobis Metric from Equivalence Constraints
A Bar-Hillel, T Hertz, N Shental… - Journal of Machine …, 2005 - cs.huji.ac.il
Many learning algorithms use a metric defined over the input space as a principal tool, and
their performance critically depends on the quality of this metric. We address the problem of …
their performance critically depends on the quality of this metric. We address the problem of …
Learning a Mahalanobis Metric from Equivalence Constraints
A Bar-Hillel, T Hertz, N Shental, D Weinshall - Journal of Machine Learning …, 2005 - jmlr.org
Many learning algorithms use a metric defined over the input space as a principal tool, and
their performance critically depends on the quality of this metric. We address the problem of …
their performance critically depends on the quality of this metric. We address the problem of …
Learning a Mahalanobis metric from equivalence constraints.
A Bar-Hillel, T Hertz, N Shental… - Journal of Machine …, 2006 - psycnet.apa.org
Many learning algorithms use a metric defined over the input space as a principal tool, and
their performance critically depends on the quality of this metric. We address the problem of …
their performance critically depends on the quality of this metric. We address the problem of …
[PDF][PDF] Learning a Mahalanobis Metric from Equivalence Constraints
A Bar-Hillel, T Hertz, N Shental, D Weinshall - Journal of Machine …, 2005 - Citeseer
Many learning algorithms use a metric defined over the input space as a principal tool, and
their performance critically depends on the quality of this metric. We address the problem of …
their performance critically depends on the quality of this metric. We address the problem of …
[PDF][PDF] Learning a Mahalanobis Metric from Equivalence Constraints
A Bar-Hillel, T Hertz, N Shental… - Journal of Machine …, 2005 - jmlr.csail.mit.edu
Many learning algorithms use a metric defined over the input space as a principal tool, and
their performance critically depends on the quality of this metric. We address the problem of …
their performance critically depends on the quality of this metric. We address the problem of …
Learning a Mahalanobis Metric from Equivalence Constraints.
A Bar-Hillel, T Hertz, N Shental… - Journal of Machine …, 2005 - search.ebscohost.com
Many learning algorithms use a metric defined over the input space as a principal tool, and
their performance critically depends on the quality of this metric. We address the problem of …
their performance critically depends on the quality of this metric. We address the problem of …
Learning a Mahalanobis Metric from Equivalence Constraints
A Bar-Hillel, T Hertz, N Shental… - The Journal of Machine …, 2005 - dl.acm.org
Many learning algorithms use a metric defined over the input space as a principal tool, and
their performance critically depends on the quality of this metric. We address the problem of …
their performance critically depends on the quality of this metric. We address the problem of …