Solving the Multiple-Instance Problem: A Lazy Learning Approach

By Jun Wang, Jean-Daniel Zucker. 2000

673 citations recorded by the source corpus.

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Citations in the corpus

Learning Theory for Distribution Regression (2014, methodology): ““Bag-of-objects” methods (MIL, classification): Despite the large number of MIL applications and the spate of heuristic solution techniques, there exist few theoretical results in the area (Auer, 1998; Long and Tan, 1998; Blum and Kalai, 1998; Babenko et al., 2011; Zhang et al., 2013; Sabato and Tis”
Weakly supervised histopathology cancer image segmentation and classification (2014, methodology): “In the machine learning community, many MIL methods have been developed in recent years such as Diverse Density (DD) (Maron and Lozano-Pérez, 1997), Citation-kNN (Wang et al., 2000), EM-DD (Zhang and Goldman, 2001), MI-Kernels (Gärtner et al.”
On classification with bags, groups and sets (2014, background): “milar assumption is made in works which regard bags as samples from probability distributions, and dene the kernel through a divergence [58, 38]. For distances, the Hausdor distance and its variants [27, 55] also introduce certain assumptions. For example, the denition d(B i;B j) = min k min l d(x i”
Hopfield Networks is All You Need (2020, unknown): “0b — Citation-kNN (Wang, 2000) 85.”
HyDR-MI: A hybrid algorithm to reduce dimensionality in multiple instance learning (2013, methodology): “Algorithms belonging to this category include multi-instance lazy learning algorithms (41), multi-instance tree learners (36; 9; 4), multi-instance rule inducers (9), multiinstance logistic regression methods (44), multi-instance neural networks (54; 55; 56; 5), multi-”
Multi-Instance Kernels (2002, methodology): “Algorithms that have been upgraded until now are: the lazy learning algorithms Bayesian-kNN and CitationkNN ( Wang & Zucker, 2000 ), the neural network MI-NN (Ramon & De Raedt, 2000), the decision tree learner RELIC (Ruo, 2001), and the rule learner (Naive)RipperMI (Chevaleyre & Zucker, 2001).”
Multi-Instance Learning : A Survey (2004, methodology): “After Dietterich et al. [10], many multi-instance learning algorithms have been developed, mainly including Diverse Density [17], Citation-kNN and Bayesian-kNN [21], Relic [20], ID3-MI and RIPPER-MI [8], EM-DD [25], BP-MIP [29][27], MI kernels [11], MI SVMs [3], and multi-instance ensembles [30].”
Bayesian multiple instance learning: automatic feature selection and inductive transfer (2008, methodology): “MIL is a natural framework to model many real-life tasks like drug activity prediction (Dietterich et al., 1997), image retrieval (Andrews et al., 2002), face Appearing in Proceedings of the 25 th International Conference on Machine Learning, Helsinki, Finland, 2008.”
Multiple instance classification: Review, taxonomy and comparative study (2013, methodology): “In addition to K-NN and SVM, in [24] the authors propose a so-c alled ‘Citation K-NN” classifier.”
M3IC: Maximum Margin Multiple Instance Clustering (2009, methodology): “By using the minimal Hausdorff distance to measure distances between bags [Wang and Zucker, 2000], the distance between these two pictures will be very low even though their actual contents (or concepts) may differ.”
Machine Learning and Radiology (2012, methodology): “As a natural extension of the classical k-nearest neighbor (k-NN) classifier, citation-kNN was proposed by Wang and Zucker (2000), in which a Hausdorff distance is used to measure the distance between bags, and both ‘‘citers’’ and ‘‘references’’ are considered in calculating neighbors.”
Maximum Margin Multiple Instance Clustering With Applications to Image and Text Clustering (2011, methodology): “, the maximal, minimal and the average Hausdorff distance [1][30][33].”
Multi-instance clustering with applications to multi-instance prediction (2009, methodology): “rithms [1], Diverse Density [14] and its variants [15,16], multi-instance lazy learning algorithms [ 17 ],”
Multiple Structure-View Learning for Graph Classification (2018, methodology): “For example, Wang [34] proposed a lazy learning k-nearest neighbor algorithm, citation-KNN.”
Dissimilarity-based people re-identification and search for intelligent video surveillance (2013, methodology): “The distance between two sets of patches X and Y corresponding to the same part of two different individuals is evaluated by the k-th Hausdorff set distance proposed by Wang and Zucker [132]:”