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Active Semi-Supervised Learning = Robust Multi-View Learning

by: Ion Muslea, Steven Minton, Craig A Knoblock

(2002), pp. 435-442.

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Abstract

In a multi-view problem, the features of the domain can be partitioned into disjoint subsets (views) that are sufficient to learn the target concept.

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ace active active_learning adaboost adaptation agglomerative agreement algorithm algorithms allocation amibguity analysis anaphora annotation argument aso author autoclass average bag-of-concepts based bayes bayesian b-cubed belief bigrams binomial biocreative bioinformatics bionlp blocked boosting bootstrapping bottleneck buffet by carlo categorization ccg chain chunking church class classification classifier clustering coalescent cobweb codes collapsed combination comlex committee comparison complete-link component compositional compound concept concept_discovery conditional confidence conformal conjugacy constrained_clustering constraints context contraints contrastive convergence conway cooccurrence coreference corpora corpus correcting correspondence cost co-testing co-training counts crf crfs criterion curation data datasets dependencies dependency detection dictionaries dimensionality dirichlet disambiguation discrete discriminative dissimilarity distance distribution distributional divergence document domain ecoc eigenvalues em english ensemble entailment entity entrez entropy error estimation evaluation event expectation expectation-maximization experiments exponential extraction factorization feature feedback fields filtering filters fisher flybase flyslip frame frames frequency functions gamma gaussian gene gene_ontology generalized generation generative geneways german gibbs gibs grammar grammatical graph graphical graph_kernel guidelines harmonic heat hedge hellinger hidden hierarchical hierarchy hmm hpsg ibp identification identifier index indexing indian induction inference infinite information information_gain interaction inter-annotator interpretation inverse iob iobew kalman kdd kernel kernels kingman kl-divergence k-means knn kullback labels language laplacian large laten latent lda learning leibler lexical lexicon likelihood lingpipe linguistic logarithmic logistic loopy lop lsi machine machines mallet markov matrix maximization maximum maxwell mcmc measure membership methods metric metrics mining mira mixture modal model modelling models monte multiclass multinomial multinomials multiple multi-task muticlass mutual_information naive name named ncbi nearest neighbors ner network nlp noise non-negative non-parametric normalization noun on-line ontology opinion output overlap parameter paraphrasing parse parser parsing partial particle part-of-speech pca pcfg people perceptron phylogenetic pitman point poisson polysemous polysemy pool positive ppi predicate prediction principal prior priors probabilistic probability process processes propaagation proper protein protein-protein qbc query query_by_committee rand random ranking rasp rcv1 recognition reduction regression reinforcement relation relational relationships resolution retrieval reusability reuters rules sampling scheme scixml scoring search seed segmentation selection selective self-training semantic semantics semi-supervised sense sentiment sequence series shallow shared similarity single-link sketch skip-chain smoothing soft softtfidf software space sparse spectral spectral_clustering speculative speech split-merge statistical stick-breaking stopping string string_kernel structural structure subcategorization summarization supertagging supervised support svm svms switching syntactic tagging task term text textual theory time timed topic training transductive transfer transformation-based tree tutorial uncertainty unigrams unlabelled unsupervised user variable variable-length variational variational_inference vector verb views v-measure walks web weighting word wordnet wsd xml yor