The nature of statistical learning theory
The nature of statistical learning theory
An introduction to Kolmogorov complexity and its applications (2nd ed.)
An introduction to Kolmogorov complexity and its applications (2nd ed.)
An elementary proof of a theorem of Johnson and Lindenstrauss
Random Structures & Algorithms
The Journal of Machine Learning Research
Kernel k-means: spectral clustering and normalized cuts
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
The dawning of the autonomic computing era
IBM Systems Journal
Algorithmic Learning in a Random World
Algorithmic Learning in a Random World
Open Set Face Recognition Using Transduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Description with Local Binary Patterns: Application to Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hedging Predictions in Machine Learning
The Computer Journal
Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions
Communications of the ACM - 50th anniversary issue: 1958 - 2008
Learning to rank with partially-labeled data
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimation of individual prediction reliability using the local sensitivity analysis
Applied Intelligence
Modeling and Reasoning with Bayesian Networks
Modeling and Reasoning with Bayesian Networks
Fast Similarity Search for Learned Metrics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ranking and semi-supervised classification on large scale graphs using map-reduce
TextGraphs-4 Proceedings of the 2009 Workshop on Graph-based Methods for Natural Language Processing
Local feature hashing for face recognition
BTAS'09 Proceedings of the 3rd IEEE international conference on Biometrics: Theory, applications and systems
A Martingale Framework for Detecting Changes in Data Streams by Testing Exchangeability
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Internet Computing
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
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The re-identification problem is to match objects across multiple but possibly disjoint fields of view for the purpose of sequential authentication over space and time. Detection and seeding for initialization do not presume known identity and allow for re-identification of objects and/or faces whose identity might remain unknown. Specific functionalities involved in re-identification include clustering and selection, recognition-by-parts, anomaly and change detection, sampling and tracking, fast indexing and search, sensitivity analysis, and their integration for the purpose of identity management. As re-identification processes data streams and involves change detection and on-line adaptation three complementary statistical learning frameworks, driven by randomness for the purpose of robust prediction, are advanced here to support the functionalities listed earlier and their combination thereof. The intertwined learning frameworks employed are those of (a) semi-supervised learning (SSL); (b) transduction; and (c) conformal prediction. The overall architecture proposed is data-driven and modular, on one side, and discriminative and progressive, on the other side. The architecture is built around autonomic computing and W5+. Autonomic computing or self-management provides for closed-loop control. W5+ answers questions related to What data to consider for sampling and collection, When to capture the data and from Where, and How to best process the data. The Who (is) query is about identity for biometrics, and the Why question for explanation purposes. The challenge addressed throughout is that of evidence-based management to progressively collect and add value to data in order to generate knowledge that leads to purposeful and gainful action including active learning for the overall purpose of re-identification. A venue for future research includes adversarial learning when re-identification is possibly ''distracted'' using deliberate corrupt information.