Learning by Distances
نویسندگان
چکیده
منابع مشابه
Learning Local Invariant Mahalanobis Distances
For many tasks and data types, there are natural transformations to which the data should be invariant or insensitive. For instance, in visual recognition, natural images should be insensitive to rotation and translation. This requirement and its implications have been important in many machine learning applications, and tolerance for image transformations was primarily achieved by using robust...
متن کاملBoosting Classification Based Similarity Learning by using Standard Distances
Metric learning has been shown to outperform standard classification based similarity learning in a number of different contexts. In this paper, we show that the performance of classification similarity learning strongly depends on the sample format used to learn the model. We also propose an enriched classification based set-up that uses a set of standard distances to supplement the informatio...
متن کاملKernel-based distances for relational learning
In this paper we present a novel and general framework for kernel-based learning over relational schemata. We exploit the notion of foreign keys to perform the leap from a flat attribute-value representation to a structured representation that underlines relational learning. We define a new attribute type which builds on the notion of foreign keys that we call instance-set. It is shown that thi...
متن کاملLearning Distances for Arbitrary Visual Features
This paper presents a method for learning distance functions of arbitrary feature representations that is based on the concept of wormholes. We introduce wormholes and describe how it provides a method for warping the topology of visual representation spaces such that a meaningful distance between examples is available. Additionally, we show how a more general distance function can be learnt th...
متن کاملLearning pullback HMM distances for action recognition
In action, activity or identity recognition it is sometimes useful, rather than to simply extract spatiotemporal features from the volumes representing motions, to explicitly encode their dynamics by means of dynamical systems. Hidden Markov models (HMMs) are a popular choice in that respect: actions can be then classified by measuring distances in the space of HMMs. However, using a fixed, arb...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Information and Computation
سال: 1995
ISSN: 0890-5401
DOI: 10.1006/inco.1995.1042