The paper that introduced Gibbs sampling.

@ARTICLE{stochastic:geman,
  author = {S. Geman and D. Geman},
  title = {Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images},
  journal = {PAMI},
  year = 1984,
  volume = {6(6)},
  pages = {721--741}
}


Original LDA paper, solved using variational methods. Complicated and hard to understand, best to stick to the Gibbs version.

@ARTICLE{blei:latent,
  author = {D. M. Blei and A. Y. Ng and M. I. Jordan},
  title = {Latent {D}irichlet Allocation},
  journal = {J. Machine Learning Research},
  year = 2003,
  volume = {3},
  pages = {993--1022}
}


LDA solved using Gibbs sampling - same as original model, just much, much simpler solution. Claims fast too, though that is debatable.

@ARTICLE{griffiths:finding,
  author = {T. L. Griffiths and M. Steyvers},
  title = {Finding scientific topics},
  journal = {Proc. Nat. Academy of Sciences, USA},
  year = 2004
}


Introduces HDP - basically LDA with a Dirichlet processes, which effectively means it automatically learns the correct number of topics.

@ARTICLE{teh:hierarchical,
  author = {Y. W. Teh and M. I. Jordan and M. J. Beal and D. M. Blei},
  title = {Hierarchical {D}irichlet Processes},
  journal = {J. American Statistical Association},
  year = 2006,
  volume = {101(476)},
  pages = {1566--1581}
}


One of the best tutorials on Dirichlet processes.

@INBOOK{teh:hierarchical2,
  author = {Y. W. Teh and M. I. Jordan},
  title = {Bayesian Nonparametrics},
  chapter = {Hierarchical Bayesian Nonparametric Models with Applications},
  publisher = {Cambridge University Press},
  year = {2010}
}


Original introduction of Chinese restaurant process.

@ARTICLE{aldous:exchangeability,
  author = {D. J. Aldous},
  title = {Exchangeability and related topics},
  journal = {Lecture Notes in Mathematics},
  year = {1985},
  volume = {1117},
  pages = {1--198}
}


Introduces Dual-HDP, an extension to the LDA idea, plus mixture versions of LDA and HDP. Two main changes exist from standard LDA in the best, Dual-HDP, formulation - firstly it clusters documents, by drawing the topic distributions from a number of distributions, where each document is presumed to belong to a single member. Secondly, it uses infinite member mixtures, so you do not have to specify either the number of topics or the number document clusters (A trick taken from HDP). Results are given for traffic stuff, including phase analysis of the MIT video.

@ARTICLE{wang:unsupervised,
  author = {X. Wang and X. Ma and E. Grimson},
  title = {Unsupervised Activity Perception in Crowded and Complicated Scenes Using Hierarchical {B}ayesian Models},
  journal = {PAMI},
  year = 2009,
  volume = {31(3)},
  pages = {539--555}
}


Takes LDA and adds temporal component between documents - each document is assumed to have a state (So documents are effectively clustered.), and there is a transition matrix from document to document over time. Solves with Gibbs sampling, demonstrates on UK traffic data.

@ARTICLE{hospedales:markov,
  author = {T. Hospedales and S. Gong and T. Xiang},
  title = {A {M}arkov Clustering Topic Model for Mining Behaviour in Video},
  journal = {ICCV},
  year = 2009
}


Defines the algorithm delta-LDA. Extension to LDA to have two sets of topics - normal and abnormal. Documents come marked, so its known if they should have the abnormal set in or not. Everything else just follows as normal LDA, solved using Gibbs sampling. Applies it to the problem of statistical debugging of software.

@ARTICLE{andrzejewski:statistical,
  author = {D. Andrzejewski and A. Mulhern and B. Liblit and X. Zhu},
  title = {Statistical Debugging using Latent Topic Models},
  journal = {ECML},
  year = 2007,
  volume = {18},
  pages = {6--17}
}


Simple approach to video behaviour anomaly detection, but demonstrates the optical flow/word approach. Generates binary motion histogram for each frame, uses as feature vector. Also adds in colour and direction of motion histograms. Clusters features using k-means to get prototype features, each clip is then a sequence of prototype features. Co-occurrence is used to define a clustering of documents, from which outlier clusters are detected and marked as anomalies. Demonstrate on various boring video sequences - poker game, traffic, hospital.

@ARTICLE{zhong:detecting,
  author = {H. Zhong and J. Shi and M. Visontai},
  title = {Detecting Unusual Activity in Video},
  journal = {CVPR},
  year = 2004,
  volume = {2},
  pages = {819--826}
}


Uses thresholding on optical flow to group moving pixels by connectivity, groups then become features built on their bounding box location and size, with ratio of foreground pixels and average motion vectors. Each clip then has k-means applied to its features to get a smaller set of less-noisy features, which are augmented with the variances of the clusters. All of these secondary features are then modelled using a mixture of Gaussians (With BIC model selection) to get a set of global clusters. The scene is then segmented, by assigning a vector to each pixel of how many times each global cluster has used each pixel (Going back to the bounding boxes), normalised. Spectral clustering is then performed, to get a behavioural segmentation of the scene. After this the clustering procedure is applied again, but in each segment, with an entropy based pruning of cluster members - all these clusters are combined to get the final set of video features. Co-occurrence modelling is then used to model them, and for outlier detection. Results shown for UK road scenes.

@ARTICLE{li:scene,
  author = {J. Li and S. Gong and T. Xiang},
  title = {Scene Segmentation for Behaviour Correlation},
  journal = {ECCV},
  year = 2008,
  pages = {383--395}
}


li:scene but using probabilistic latent scene analysis for the correlation modelling.

@ARTICLE{li:global,
  author = {J. Li and S. Gong and T. Xiang},
  title = {Global Behaviour Inference using Probabilistic Latent Semantic Analysis},
  journal = {BMVC},
  year = 2008,
  pages = {193--202}
}


Topic modelling with simple temporal model using n-gram statistics.

@ARTICLE{wallach:topic,
  author = {H. M. Wallach},
  title = {Topic Modeling: Beyond Bag-of-Words},
  journal = {ICML},
  year = 2006,
  volume = {23},
  pages = {977--984}
}


Uses the Dual-HDP topic model, but with tracking. Takes tracks and turns them into documents, by quantising location and direction onto the image grid where the track passes. Tested on radar as well as traffic. Has the interesting property that topics will form individual actions, of which any given track can perform a set of. Will not detect abnormal interactions, only abnormal routes, and suffers from all the tracking problems.

@ARTICLE{wang:trajectory,
  author = {X. Wang and K. T. Ma and G-W. Ng and E. Grimson},
  title = {Trajectory analysis and Semantic region Modeling Using A Nonparametric {B}ayesian Model},
  journal = {CVPR},
  year = 2008,
  pages = {1--8}
}


Finds special points in the scene using tracking. Entry and exit points are found by fitting a Gaussian mixture model to the start/end of tracks, with outlier detection and cluster size thresholding. Stopping points are found by thresholding speed of tracks and then doing the same as entry/exit points with the resulting sampled locations. Routes that people travel on are found by merging tracks that have envelopes, indicating the range of variability in the route.

@ARTICLE{makris:learning,
  author = {D. Makris and T. Ellis},
  title = {Learning Semantic Scene Models From Observing Activity in Visual Surveillance},
  journal = {Systems, Man, and Cyber., B},
  year = 2005,
  volume = {35(3)},
  pages = {397--408}
}


Uses two clustering techniques on tracks to build its model. First clustering divides objects into classes, i.e. vehicles and pedestrians. Second subdivides these into activities. Uses spectral clustering. Clusters of tracks effectively good routes through the scene. Identifiers sources and sinks by clustering start/end points, using a Gaussian mixture model. Identifiers outliers in terms of not following the routes as abnormalities.

@ARTICLE{wang:learning,
  author = {X. Wang and K. Tieu and E. Grimson},
  title = {Learning Semantic Scene Models by Trajectory Analysis},
  journal = {ECCV},
  year = 2006,
  pages = {110--123}
}


Constructs per-pixel PDF's over speed, size and destination of tracks that pass over each pixel (uses all pixels in bounding box of object.). PDF is Gaussian mixture model. Constructs over multiple temporal scales, so each track can be accessed for outlier detection on actions that take different amounts of time.

@ARTICLE{basharat:learning,
  author = {A. Basharat and A. Gritai and M. Shah},
  title = {Learning Object Motion Patterns for Anomaly Detection and Improved Object Detection},
  journal = {CVPR},
  year = 2008,
  pages = {1--8}
}


Very focused on tracker. Gives background subtraction using updating mixture of Gaussians. Finds objects via connected components. Kalman filter to link detections to make tracks. Its then clusters tracks and builds co-occurrence matrix. Does some hierarchy stuff to classify object and uses co-occurrence matrix in combination with outliers from the clustering to detect anomalies.

@ARTICLE{stauffer:learning,
  author = {C. Stauffer and E. Grimson},
  title = {Learning Patterns of Activity Using Real-Time Tracking},
  journal = {PAMI},
  year = 2000,
  volume = {22},
  pages = {747--757}
}


Uses background subtraction and connected components to find blobs, represents them by a feature vector of position, bounding box, percent foreground pixels etc. and clusters using Gaussian mixture model with BIC model selection. This then allows each frame to be represented as a feature vector containing the probabilities that each of the clusters was seen. Each clip is then modelled using a dynamic Bayesian network, (Specifically a multi-Observation hidden Markov model.) an affinity between models is defined and spectral clustering is used to cluster the clips. Abnormality detection is based on outliers from this final model, with a one way temporal smoothing.

@ARTICLE{xiang:video,
  author = {T. Xiang and S. Gong},
  title = {Video Behaviour Profiling for Anomaly Detection},
  journal = {PAMI},
  year = 2008,
  volume = {30(5)},
  pages = {893--908}
}


@ARTICLE{larlus:category,
  author = {D. Larlus and J. Verbeek and F. Jurie},
  title = {Category Level Object Segmentation by Combining Bag-of-Words Models with {D}irichlet Processes and Random Fields},
  journal = {IJCV},
  year = 2010,
  volume = {88(2)},
  pages = {238--253}
}


@ARTICLE{philbin:geometric,
  author = {J. Philbin and J. Sivic and A. Zisserman},
  title = {Geometric {LDA}: A Generative Model for Particular Object Discovery},
  journal = {BMVC},
  year = 2008
}


A topic model that includes an implicit shape model.

@ARTICLE{wong:learning,
  author = {S-F. Wong and T-K. Kim and R. Cipolla},
  title = {Learning Motion Categories using both Semantic and Structural Information},
  journal = {CVPR},
  year = 2007,
  pages = {1--6}
}


@ARTICLE{wang:spatial,
  author = {X. Wang and E. Grimson},
  title = {Spatial Latent {D}irichlet Allocation},
  journal = {NIPS},
  year = 2007
}


Most recent (as of Feb 2011) delta LDA paper, i.e. LDA with semi-supervised learning.

@ARTICLE{li:learning,
  author = {Jian Li and Timothy Hospedales and Shaogang Gong and Tao Xiang},
  title = {Learning Rare Behaviours},
  journal = {ACCV},
  year = 2010
}


Article that gives multiple way of inferring the likelihood of a new document given a topic model - includes the left-to-right algorithm that I typically use.

@ARTICLE{wallach:evaluation,
  author = {H. M. Wallach and I. Murray and R. Salakhutdinov and D. Mimno},
  title = {Evaluation Methods for Topic Models},
  journal = {ICML},
  year = 2009,
  volume = {26},
  pages = {1105--1112}
}


Below is as the title suggests. It is the best reference available, being a technical report, but there is actually a better written piece called 'Maximum Likelihood Estimation of Dirichlet Distribution Parameters' by J. Huang, the title of which gives away the rest of the pertinent details.

@TECHREPORT{minka:estimating,
  author = {T. P. Minka},
  title = {Estimating a Dirichlet distribution},
  institution = {MIT},
  year = {2000}
}


Provides the method of sampling a concentration parameter that is used by a single DP. For the method where a concentration parameter is shared by multiple DPs see the Hierarchical Dirichlet Processes paper by Teh et al.. Actual paper focuses on basic 1D desntity estimation with Gaussian distributions.

@ARTICLE{escobar:density,
  author = {M. D. Escobar and M. West},
  title = {Bayesian Density Estimation and Inference Using Mixtures},
  journal = {J. American Statistical Association},
  year = 1995,
  volume = {90(430)},
  pages = {577--588}
}


Original introduction of the Chinese restaurant process, albeit as the Polya Urn scheme.

@ARTICLE{blackwell:ferguson,
  author = {D. Blackwell and J. B. MacQueen},
  title = {Ferguson Distributions Via {P}olya Urn Schemes},
  journal = {Annals of Statistics},
  year = 1973,
  volume = {1(2)},
  pages = {353--355}
}


The original Dirichlet process paper.

@ARTICLE{ferguson:bayesian,
  author = {T. S. Ferguson},
  title = {A {B}ayesian Analysis of Some Nonparametric Problems},
  journal = {The Annals of Statistics},
  year = {1973},
  volume = {1(2)},
  pages = {209--230}
}


The paper that introduces the stick-breaking process as an explanation of the Dirichlet process. Also includes extra properties not observed by Ferguson.

@ARTICLE{sethuraman:constructive,
  author = {J. Sethuraman},
  title = {A constructive definition of {D}irichlet priors},
  journal = {Statistica Sinica},
  year = {1994},
  volume = {4},
  pages = {639--650}
}


Includes incremental methods for LDA, such as the incremental Gibbs sampling method.

@ARTICLE{canini:online,
  author = {K. R. Canini and L. Shi and T. L. Griffiths},
  title = {Online Inference of Topics with Latent Dirichlet Allocation},
  journal = {Proc. AI Stats},
  year = 2009
}


@ARTICLE{xiang:incremental,
  author = {T. Xiang and S. Gong},
  title = {Incremental and Adaptive Abnormal Behaviour Detection},
  journal = {Computer Vision and Image Understanding},
  year = {2008},
  volume = {111},
  pages = {59--73},
  number = {1}
}


@ARTICLE{kuettel:what,
  author = {D. Kuettel and M. D. Breitenstein and L. V. Gool and V. Ferrari},
  title = {What's going on? Discovering Spatio-Temporal Dependencies in Dynamic Scenes},
  journal = {CVPR},
  year = 2010
}


@ARTICLE{saleemi:scene,
  author = {I. Saleemi and L. Hartung and M. Shah},
  title = {Scene Understanding by Statistical Modeling of Motion Patterns},
  journal = {CVPR},
  year = 2010
}


@ARTICLE{feifei:one-shot,
  author = {L. Fei-Fei and R. Fergus and P. Perona},
  title = {One-Shot Learning of Object Categories},
  journal = {PAMI},
  year = 2006,
  volume = 28,
  pages = {594--611},
  number = 4
}


Probably the best literature survey of active learning, at least in 2011.

@TECHREPORT{settles:active,
  author = {B. Settles},
  title = {Active Learning Literature Survey},
  year = {2009},
  institution = {Uni. of Wisconsin-Madison},
  number = {1648}
}


Another literature review of active learning.

@TECHREPORT{olsson:literature,
  author = {F. Olsson},
  title = {A literature survey of active machine learning in the context of natural language processing},
  year = {2009},
  institution = {Swedish Institute of Computer Science},
  number = {T2009:06}
}


Whilst not the earliest active learning paper probably the best reference for introducing it, as it formally stated the problem and tackled it analytically - earlier approach were very problem specific.

@ARTICLE{angluin:queries,
  author = {D. Angluin},
  title = {Queries and Concept Learning},
  journal = {Machine Learning},
  year = {1988},
  volume = {2(4)},
  pages = {319--342}
}


Introduces the query by committee method of active learning - an uncertainty method based on having a committee of models and querying instances about which the committee has the greatest disagreement.

@ARTICLE{seung:query,
  author = {H. S. Seung and M. Opper and H. Sompolinsky},
  title = {Query by Committee},
  journal = {Proc. Workshop on Computational Learning Theory},
  year = {1992},
  volume = {5},
  pages = {287--294}
}


Early if not the first example of uncertainty sampling for active learning based (implicitly) on the concept of calculating the probability of getting a sample wrong.

@ARTICLE{lewis:sequential,
  author = {D. D. Lewis and W. A. Gale},
  title = {A Sequential Algorithm for Training Text Classifiers},
  journal = {Proc. Conf. on Research and Development in Information Retrieval},
  year = {1994},
  volume = {17},
  pages = {3--12}
}


@ARTICLE{culotta:reducing,
  author = {A. Culotta and A. McCallum},
  title = {Reducing labeling effort for structured prediction tasks},
  journal = {Proc. Nat. Conf. Artificial Intelligence},
  year = {2005},
  pages = {746--751}
}


Uses SVM's; defines version space of answers for hyperplane positioning, does active learning by selecting the exemplars that can shrink the version space the fastest. Prevents the use of the kernel trick and requires linear seperability, which kinda ruins the entire point of using an SVM. Also allows for transduction, where it also uses unlabeled exemplars.

@ARTICLE{tong:support,
  author = {S. Tong and D. Koller},
  title = {Support Vector Machine Active Learning with Applications to Text Classification},
  journal = {ICML},
  year = {2000},
  volume = {2},
  pages = {45--66}
}


Unsupervised visual category discovery, but with some supervision in the form of some categories being trained and some not. It uses the spatial relationships between trained and untrained categories (segmented image, graph structure.) to help automatically learn the untrained. Designed to be used in an active learning loop. The classification of known/unknown is done by thresholding entropy of class assignment distribution from classifier. (SVM, no indication how it gets probabilities.)

@ARTICLE{lee:object,
  author = {Y. J. Lee and K. Grauman},
  title = {Object-Graphs for Context Aware Category Discovery},
  journal = {CVPR},
  year = {2010},
  pages = {1--8}
}


Probably the first paper that covers active learning with an emphasis on rare categories. Includes a nice summary of the weaknesses/strengths of the 'standard' techniques and a nice example involving galaxies of extremely unbalanced classes. Actual method is an EM classifier using Gaussian distributions - they run a semi-supervised fitting and for each cluster order the points for which it is the best fit based on how strongly they are assigned to the cluster. They then interleave these lists from all known clusters, starting with the worst fit and use that.

@ARTICLE{pelleg:active,
  author = {D. Pelleg and A. Moore},
  title = {Active Learning for Anomaly and Rare-Category Detection},
  journal = {Advances in Neural Information Processing Systems},
  year = {2004},
  volume = {17},
  pages = {1073--1080}
}


Active learning method based on nearest neighbour hacks. Basically a density estimate based on the assumed number of normal vs. abnormal classes. Everything is hacked - a case of brute force engineering rather than any insight or science. No consideration of the current model is used.

@ARTICLE{he:nearest,
  author = {J. He and J. G. Carbonell},
  title = {Nearest-Neighbor-Based Active Learning for Rare Category Detection},
  journal = {Neural Information Processing Systems},
  year = {2007},
  volume = {21}
}


A hierarchical mean shift based approach for active learning. Very clever - takes a good example for each mode for learning, working from a large kernel down to a small kernel so that by the end each exemplar is its own mode. Bloody impressive results for rare class discovery, but no good for improving the classifier rate - does not consider the current model.

@ARTICLE{vatturi:category,
  author = {P. Vatturi and W-K. Wong},
  title = {Category Detection Using Hierarchical Mean Shift},
  journal = {Knowledge Discovery and Data mining},
  year = {2009},
  volume = {15},
  pages = {847--856}
}


As the title says - nothing to do with active learning but certainly covers its subject area.

@ARTICLE{hodge:survey,
  author = {V. Hodge and J. Austin},
  title = {A Survey of Outlier Detection Methodologies},
  journal = {Artificial Intelligence Review},
  year = {2004},
  volume = {22(2)},
  pages = {85--126}
}


A model mixing approach to active learning - uses both a generative model with outlier detection and a discriminative model with uncertainty detection, at the same time. Model switching is used, highly heuristic, though decent results. Lots of parameters.

@ARTICLE{hospedales:finding,
  author = {T. M. Hospedales and S. Gong and T. Xiang},
  title = {Finding Rare Classes: Adapting Generative and Discriminative Models in Active Learning},
  journal = {Pacific-Asia Conference on Knowledge Discovery and Data Mining},
  year = {2011},
  volume = {15}
}


Contains the iris data set - very old but easy, small problem that is good for testing.

@ARTICLE{fisher:use,
  author = {R. A. Fisher},
  title = {The use of multiple measurements in taxonomic problems},
  journal = {Annals of Eugenics},
  year = {1936},
  volume = {7(2)},
  pages = {179--188}
}


@TechReport{huang:labeled,
  author = {G. B. Huang and M. Ramesh and T. Berg and E. Learned-Miller},
  title = {Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments},
  institution = {University of Massachusetts, Amherst},
  year = 2007,
  number = {07-49}
}

Uses the 'Labeled Faces in the Wild' data set, as extracted from Yahoo!News, and is a variant of that data set that I have used.

@ARTICLE{guillaumin:is,
  author = {M. Guillaumin and J. Verbeek and C. Schmid},
  title = {Is that you? Metric learning approaches for face identification},
  journal = {ICCV},
  year = {2009}
}



Sun dataset - scene recognition, very big.

@ARTICLE{xiao:sun,
  author = {J. Xiao and J. Hays and K. Ehinger and A. Oliva and A. Torralba},
  title = {SUN Database: Large-scale Scene Recognition from Abbey to Zoo},
  journal = {CVPR},
  year = {2010}
}


Incremental kernel density estimation with constant time - basically a KDE that optimally merges clusters so the number is capped. Uses a Gaussian kernel.

@ARTICLE{sillito:incremental,
  author = {R. R. Sillito and R. B. Fisher},
  title = {Incremental One-Class Learning with Bounded Computational Complexity},
  journal = {International Conference on Artificial Neural Networks},
  year = {2007},
  volume = {17},
  pages = {58--67}
}


Reference for UCI data repository - good source of data sets.

@misc{frank:uci,
  author = {A. Frank and A. Asuncion},
  year = {2010},
  title = {{UCI} Machine Learning Repository},
  url = {http://archive.ics.uci.edu/ml},
  institution = {University of California, Irvine, School of Information and Computer Sciences}
}


Reference for the digits data set.

@misc{lecun:digits,
  author = {Y. Le{C}un and C. Cortes},
  title = {MNIST database of handwritten digits},
  url = {http://yann.lecun.com/exdb/mnist/},
}


Reference for the gait data set.

@misc{zheng:gait,
  author = {S. Zheng},
  title = {CASIA Gait Database},
  url = {http://www.cbsr.ia.ac.cn/english/Gait\%20Databases.asp},
}


Paper that describes the algorithm used to pre-process the gait videos into the gait energy images used by the gait data set.

@ARTICLE{han:individual,
  author = {J. Han and B. Bhanu},
  title = {Individual Recognition Using Gait Energy Image},
  journal = {Pattern Analysis and Machine Intelligence},
  year = {2006},
  volume = {28(2)},
  pages = {316--322}
}


Active learning algorithm that combines the goals of finding new classes and refining the boundaries between classes. Does interleaving between two algorithms, one to achieve each goal, noting that it is already using an interleaving algorithm for rare class detection - end result is large numbers of instances are provided in each batch, and not very intelligently. It also has a stupid name.

@TECHREPORT{stokes:aladin,
  author = {J. W. Stokes and J. C. Platt and J. Kravis and M. Shilman},
  title = {{ALADIN}: Active Learning of Anomalies to Detect Intrusion},
  institution = {Microsoft Research},
  year = {2008},
  number = {2008-24}
}


Paper that introduced the concept of active learning via expected error reduction.

@ARTICLE{roy:toward,
  author = {N. Roy and A. McCallum},
  title = {Toward optimal active learning through sampling estimation of error reduction},
  journal = {ICML},
  year = {2001},
  pages = {441--448}
}


Paper that introduces expected model change for active learning.

@ARTICLE{settles:multiple,
  author = {B. Settles and M. Craven and S. Ray},
  title = {Multiple-instance Active Learning},
  journal = {NIPS},
  year = {2008},
  volume = {20},
  pages = {1289--1296}
}


@ARTICLE{haines:active,
  author = {T. S. F. Haines and T. Xiang},
  title = {Active Learning using Dirichlet Processes for Rare Class Discovery and Classification},
  journal = {BMVC},
  year = {2011}
}

 
Version of my active learning algorithm that works with streams instead of pools and using a Pitman-Yor process instead of a Dirichlet process. Also has some query by committee stuff.

@ARTICLE{loy:stream,
  author = {C. C. Loy and T. M. Hospedales and T. Xiang and S. Gong},
  title = {Stream-based Joint Exploration-Exploitation Active Learning},
  journal = {CVPR},
  year = {2012}
}


Tim's expected error reduction variant of my P(wrong) code.
Does the obvious thing, though has to fallback to p(wrong) due to computational insanity.

@ARTICLE{hospedales:unifying,
  author = {T. M. Hospedales and S. Gong and T. Xiang},
  title = {A Unifying Theory of Active Discovery and Learning},
  journal = {ECCV},
  year = {2012}
}


Introduces the concept of random decision forests

@ARTICLE{ho:random,
  author = {T. K. Ho},
  title = {Random Decision Forests},
  journal = {Proc. Document Analysis and Recognition},
  year = {1995},
  volume = {1},
  pages = {278--282}
}


Improvements of original random decision forests idea, often considered to be the first paper, though I can't figure out why.

@ARTICLE{breiman:random,
  author = {L. Breiman},
  title = {Random Forests},
  journal = {Machine Learning},
  year = {2001},
  volume = {45(1)},
  pages = {5--32}
}


The extreme random forests stuff - even more randomness than before!

@ARTICLE{geurts:extremely,
  author = {P. Geurts and D. Ernst and L. Wehenkel},
  title = {Extremely randomized trees},
  journal = {Machine Learning},
  year = {2006},
  volume = {63(1)},
  pages = {3--42}
}


Incremental decision trees paper.

@ARTICLE{basak:online,
  author = {J. Basak},
  title = {Online Adaptive Decision Trees: Pattern Classification and Function Approximation},
  journal = {Neural Computation},
  year = {2006},
  volume = {18(9)},
  pages = {2062--2101}
}


Online decision forests paper - has the bootstrap trick, growing new trees and growing trees incrementally.

@ARTICLE{saffari:online,
  author = {Amir Saffari and Christian Leistner and Jakob Santner and Martin Godec and Horst Bischof},
  title = {Online Random Forests},
  journal = {ICCV-OLCV},
  year = {2009}
}


Gives the equivalence between a bootstrap draw and selecting each item Poisson(1) times.

@ARTICLE{oza:online,
  author = {N. C. Oza and Stuart Russell},
  title = {Online Bagging and Boosting},
  journal = {Artificial Intelligence and Statistics},
  year = {2001},
  pages = {105--112}
}


Book that makes a good reference for decision trees.

@BOOK{breiman:classification,
  author = {L. Breiman and J. Friedman and C. J. Stone and R. A. Olshen},
  title = {Classification and Regression Trees},
  publisher = {Chapman and Hall/CRC},
  year = {1984}
}


Microsofts decision forest tutorial.

@TECHREPORT{criminisi:decision,
  author = {A. Criminisi and J. Shotton and E. Konukoglu},
  title = {Decision Forests for Classification, Regression, Density Estimation, Manifold Learning and Semi-Supervised Learning},
  institution = {Microsoft Research},
  year = {2011},
  number = {MSR-TR-2011-114}
}


Active learning for regression.

@ARTICLE{mackay:information,
  author = {D. J. C. MacKay},
  title = {Information-based Objective Functions for Active Data Selection},
  journal = {Neural Computation},
  year = {1992},
  volume = {4(4)},
  pages = {590--604}
}


A query by committee method, based on entropy of votes.

@ARTICLE{dagan:committee,
  author = {I. Dagan and S. Engelson},
  title = {Committee-Based Sampling for Training Probabilistic Classifiers},
  journal = {ICML},
  year = {1995},
  pages = {150--157}
}


Query by committee using boosting and bagging based models to provide the committee, due to their random nature.

@ARTICLE{abe:query,
  author = {N. Abe and H. Mamitsuka},
  title = {Query learning strategies using boosting and bagging},
  journal = {ICML},
  year = {1998},
  pages = {1--9}
}


Query by committee using Kullback-Leiber divergence between the averaged and voted probability distributions generated by the committee.

@ARTICLE{mccallum:employing,
  author = {A. McCallum and K. Nigam},
  title = {Employing EM in pool-based active learning for text classification},
  journal = {ICML},
  year = {1998},
  pages = {359--367}
}


Early example of stream based active learning.

@ARTICLE{cohn:improving,
  author = {D. Cohn and L. Atlas and R. Ladner},
  title = {Improving Generalization with Active Learning},
  journal = {Machine Learning},
  year = {1994},
  volume = {15(2)},
  pages = {201--221}
}


Review paper for reinforcement learning.

@ARTICLE{kaelbling:reinforcement,
  author = {L. P. Kaelbling and M. L. Littman and A. W. Moore},
  title = {Reinforcement Learning: A Survey},
  journal = {J. Artificial Intelligence Research},
  year = {1996},
  volume = {4},
  pages = {237--285}
}


Original SVM paper.

@ARTICLE{boser:training,
  author = {B. E. Boser and I. Guyon and V. Vapnik},
  title = {A Training Algorithm for Optimal Margin Classifiers},
  journal = {Computational Learning Theory},
  year = {1992},
  volume = {5},
  pages = {144--152}
}


How to get probabilities from an SVM.

@ARTICLE{platt:probabilistic,
  author = {J. C. Platt},
  title = {Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods},
  journal = {Advances in large margin classifiers},
  year = {1999},
  pages = {61--74}
}



Introduces the concept of generalisation being a search for a suitable classification function; includes the concept of version spaces.

@ARTICLE{mitchell:generalization,
  author = {T. M. Mitchell},
  title = {Generalization as Search},
  journal = {Artificial Intelligence},
  year = {1982},
  volume = {18},
  pages = {203--226}
}


Cost benefit analysis - original article!

@ARTICLE{dupuit:measurement,
  author = {J. Dupuit},
  title = {On the Measurement of the Utility of Public Works},
  journal = {International Economic Papers},
  year = {1952},
  volume = {2},
  pages = {83--110},
  notes = {Translation of 1844 article}
}


Papaer that uses Dirichlet process for semi-supervised classification whilst using entropy-based active learning. Classification is kinda fun (Gibbs sampling CRP with constraints that certain pairs must/must not belong to the same table.), but pre-existing; active learning part is suposed to be the contribution, but is trivial and boring.

@ARTICLE{vlachos:active,
  author = {A. Vlachos and Z. Ghahramani and T. Briscoe},
  title = {Active Learning for Constrained Dirichlet Process Mixture Models},
  journal = {Proc. 2010 Workshop on GEometrical Models of Natural Language Semantics},
  year = {2010},
  pages = {57--61}
}


Example of imbalanced data - oil spills.

@ARTICLE{kubat:machine,
  author = {M Kubat and R. C. Holte and S. Matwin},
  title = {Machine Learning for the Detection of Oil Spills in Satellie Radar Images},
  journal = {Machine Learning},
  year = {1998},
  volume = {30},
  pages = {195--215}
}


Example of imbalanced data - rooftop detection.

@ARTICLE{maloof:improved,
  author = {M. A. Maloof and P. Langley and T. O. Binford and R. Nevatia and S. Sage},
  title = {Improved Rooftop Detection in Aerial Images with Mahcine Learning},
  journal = {Machine Learning},
  year = {2003},
  volume = {53},
  pages = {157--191}
}


Hierachical CRFs paper.

@ARTICLE{ladicky:associative,
  author = {L. Ladick\'{y} and C. Russell and P. Kohli and P. H. S. Torr},
  title = {Associative Hierarchical CRFs for Object Class Image Segmentation},
  journal = {ICCV},
  year = {2009},
  volume = {12},
  pages = {739--746}
}


An early regference to using the terms 'things' and 'stuff' in computer vision. (There are some earlier references, but they are all the same authors in the same chain of work. There is also a book that mentions it in 1991, according to Google, but I have been unable to read it.)

@ARTICLE{picard:vision,
  author = {R. W. Picard and T. P. Minka},
  title = {Vision Texture for Annotation},
  journal = {Multimedia Systems},
  year = {1995},
  volume = {3(1)},
  pages = {3--14}
}


The Grabcut paper - graph cuts at interactive speeds for interactive segmentaiton of an image.

@ARTICLE{rother:grabcut,
  author = {C. Rother and V. Kolmogorov and A. Blake},
  title = {“GrabCut” — Interactive Foreground Extraction using Iterated Graph Cuts},
  journal = {SIGGRAPH},
  year = {2004},
  volume = {23(3)},
  pages = {309--314}
}



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