Multiple Instance Regression

By Soumya Ray, David Page. 2001

163 citations recorded by the source corpus.

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Citations in the corpus

Learning Theory for Distribution Regression (2014, methodology): “On the machine learning side, multiple instance learning (Dietterich et al., 1997; Ray and Page, 2001; Dooly et al., 2002) can be thought of in this way, where each instance in a labeled bag is an i.”
Multi-instance learning by treating instances as non-I.I.D. samples (2008, background): “The main diﬀerence between standard multi-instance learning and generalized multi-instance learning is that in standard multi-instance learning there is a single concept, and a bag is positive if it has an instance satisﬁes this concept; while in generalized multi-instance learning [30, 23] there…”
Multi-Instance Kernels (2002, methodology): “For example, by simply plugging our kernel into SVM regression, the only very recently formulated problem of MI regression can be tackled (Amar et al., 2001; Ray & Page, 2001).”
Maximum Margin Multiple Instance Clustering With Applications to Image and Text Clustering (2011, background): “Most of the MIL research focuses on solving binary classification [2][7][20] and regression problems [1][21][34].”
Multi-instance clustering with applications to multi-instance prediction (2009, background): “Keywords Machine learning · Multi-instance learning · Clustering · Representation transformation”
EM-DD: An Improved Multiple-Instance Learning Technique (2001, methodology): “The basic idea behind EM-DD is to view the knowledge of which instance corresponds to the label of th e bag as a missing attribute which can be estimated using EM approach in a way similar to how EM is used in the MI regression [10].”
A review of multi-instance learning assumptions (2010, background): “At the International Conference on Machine Learning in 2001, Ray and Page (2001) and Amar et al .”
Two-stage sampled learning theory on distributions (2014, background): “On the machine learning side, multiple instance learning [2, 3, 4] can be thought of in this way, in the case where each instance in a labeled bag is an i.”
Multi-instance multi-label learning (2008, background): “Later, multi-instance regression with real-valued outputs was studied [2,52], and different versions of generalized multi-instance learning have been defined [58, 68].”
Multiple instance learning: A survey of problem characteristics and applications (2016, background): “This instance may be the closest to a target concept [36], or the best fit in a regression model [37].”
Multi-Instance Learning : A Survey (2004, methodology): “Since the training process of the BP-MIP algorithm is almost the same as that of the classical BP algorithm, it is evident that the key of BP-MIP lies in the formal definition of the multi-instance error function 1 , which is the function used to measure the error of the neural network and…”
Multiple-Instance Learning of Real-Valued Data (2001, methodology): “Ray and Page (2001) studied multiple-instance linear regression using artificial data to empirically evaluate their algorithm, which uses an inductive logic programming based approach combined with a linear regression algorithm supplemented with EM.”
Fast Bundle Algorithm for Multiple-Instance Learning (2012, background): “Index Terms—Artificial intelligence, machine learning, nonsmooth optimization, bundle methods, multiple-instance learning, ranking, medicine and science.”
Multiple-instance ranking: Learning to rank images for image retrieval (2008, methodology): “We can interpret this iterative algorithm from the perspective of EM algorithm, which has been exploited by other MI learning algorithms, such as MI regression [19], EM-DD [24] and MI-SVM [2].”
Multiple Instance Learning: Algorithms and Applications (2008, background): “Note that the MIL problem is deﬁned only for binary cases (although extensions have also been explored [5, 6]).”
Multiple instance classification: Review, taxonomy and comparative study (2013, methodology): “As analyzed in [10], the types of solutions can be categorized in a similar manner to the one employed for MIC (including instance-level solutions using the standard MI assumption [38], embedded-space solutions [35] and bag-space solutions [39]).”