A. Blum and A. Kalai. A note on learning from multiple-instance examples. Machine Learning, vol.30, no.1, pp.23--29, 1998.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....are not independent then APR learning under the multi instance learning framework is NP hard. Moreover, they presented a theoretical algorithm that does not require product distribution. Later, the theoretical algorithm was transformed to a practical algorithm named MULTINST [2] Blum and Kalai [5] described a reduction from the problem of PAC leaming under the multi instance learning framework to PAC leaming with one sided random classification noise, and presented a theoretical algorithm with smaller sample complexity than that of Auer et al. s algorithm. Among practical multi instance ....

A. Blum and A. Kalai. "A note on learning from multiple-instance examples," Machine Learning, vol.30, no. 1, pp.23-29, 1998.

....the single tuple bias. In particular, Dietterich et al. 8] have designed APR algorithms to solve the task of predicting whether a molecule is musky or not. Other APR algorithms such MULTIINST [3] have been tested on this learning task, and many interesting learnability results have been obtained [5, 2]. More recently, Maron et al. proposed a new multiple instance algorithm called Diverse Density [12] which they applied to image classification. Finally, the lazy learning approach to multiple instance learning has been investigated by Jun et al. 16] The algorithms mentioned here do not ....

Avrim Blum and Adam Kalai. A note on learning from multiple-instance examples. Machine Learning, 30, 1998.

....on object and that the suffix j of 1 to # i given to instances instance i,j is purely arbitrary. Note that in the limited theoretical research that has been done on the PAClearnability of this problem, the number vi is equal to a constant r (Long and Tan 1996; Auer 1997; Auer, Long et al. 1997; Blum and Kalai 1997). In the multiple instance framework, Dietterich et al. 1997) suggest that if the result of f is positive for an object it is because at least one of its instances ij has produced this result. If the result is negative it means that none of its instances can produce a positive result. The ....

....among # i shapes when introduced into the keyhole and that it is useful if one of the shapes it can take opens the door. This variable character of the measurements of the same object in the MIP problem means we can interpret MIP as a classical mono instance problem that has been made ambiguous (Blum and Kalai 1997). The label of the object is not associated with one single description of the object (a magic key, a molecule) but with several descriptions which are all of the same object but which represent different states of the object (a key, a configuration) these different states all being potential ....

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Blum, A. and A. Kalai 1997. "A note on learning from multiple-instances examples" Machine Learning .

....combination of lazy and eager multiple instance problem classifiers. 1. Introduction The multiple instance problem or multiple instance learning is receiving growing attention in the machine learning research field (Dietterich, Lathrop, LozanoP rez, 1997; Zucker Ganascia, 1996; Auer, 1997; Blum Kalai, 1998; Maron, 1998; De Raedt, 1998; Ruffo, 2000) Most of the work in machine learning is focused on supervised learning where each example is labeled by a teacher. In multiple instance learning, the teacher labels examples that are sets (also called bags) of instances. The teacher does not label ....

Blum, A., & Kalai, A. (1998). A note on learning from multiple-instance examples. Machine Learning, 30, 23-- 29.

....value would be used as the label. Recently, there has a been a significant amount of work in studying the multiple instance model (Wang and Zucker, 2000; Long and Tan, 1998; Auer et al. 1998; Auer, 1997; Goldman et al. 2000a; Maron and Lozano Perez, 1998; Maron, 1998; Maron and Ratan, 1998; Blum and Kalai, 1998), yet al..l of this work has only considered boolean classification. One of the contributions of our work is initiating the study of a real valued multiple instance model. In the standard multiple instance model, an example contains m points from some domain X . Most typically, X = # d . In ....

....the maximum function is used. 4. Related Learning Models Recently, there has a been a significant amount of work in studying the multiple instance model (Wang and Zucker, 2000; Long and Tan, 1998; Auer et al. 1998; Auer, 1997; Maron and Lozano Perez, 1998; Maron, 1998; Maron and Ratan, 1998; Blum and Kalai, 1998) in the boolean domain. Most of this work addresses the problem of learning a multiple instance concept defined by a single axis aligned box. There also has been some work on learning geometric patterns (Goldman et al. 2000a; Goldman and Scott, 1999; Goldberg et al. 1996) in the boolean domain ....

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Blum, A. and A. Kalai: 1998, `A note on learning from multiple-instance examples'. Machine Learning 30, 23--29.

....distribution and which takes only O i n 3 r 2 ffl 2 j time. This algorithm can be modified slightly to achieve similar results in the statistical query model; applying the results of Kearns [21] this implies that it can be made robust against classification noise. Blum and Kalai [8] recently improved on these results. Our algorithm is substantially different from those previously proposed for this problem [12, 26] We believe that a variant of our algorithm will prove useful in practice. Initial empirical results [7] support this belief: a straightforward implementation of a ....

A. Blum and A. Kalai. A note on learning from multiple-instance examples. Machine Learning, 30: 23--29, 1998.

....whether a molecule would bind at a particular site. They argued empirically that axis parallel rectangles are good hypotheses for this and other similar learning problems. Subsequently, multi instance learning has been extensively studied (e.g. Long Tan, 1998; Auer et al. 1997; Auer, 1997; Blum Kalai, 1998; Maron LozanoP erez, 1998; Maron, 1998; Maron Ratan, 1998; Blum Kalai, 1998) In several of these papers, the target concept is a single axis parallel box and a bag is classified as positive if at least one of the points in the bag is inside the target box. But any algorithm for learning ....

....axis parallel rectangles are good hypotheses for this and other similar learning problems. Subsequently, multi instance learning has been extensively studied (e.g. Long Tan, 1998; Auer et al. 1997; Auer, 1997; Blum Kalai, 1998; Maron LozanoP erez, 1998; Maron, 1998; Maron Ratan, 1998; Blum Kalai, 1998). In several of these papers, the target concept is a single axis parallel box and a bag is classified as positive if at least one of the points in the bag is inside the target box. But any algorithm for learning geometric patterns (e.g. Section 4.3) learns a union of axis parallel boxes where a ....

Blum, A., & Kalai, A. (1998). A note on learning from multipleinstance examples. Machine Learning, 30, 23--29.

....with the drug activity prediction problem described above. This work was followed by [Long and Tan, 1996] where a high degree polynomial PAC bound was given for the number of examples needed to learn in the multiple instance learning model. Auer, 1997] gives a more efficient algorithm, and [Blum and Kalai, 1998] shows that learning from multiple instance examples is reducible to PAC learning with two sided noise and to the Statistical Query model. Unfortunately, the last three papers make the restrictive assumption that all instances from all bags are generated independently. In this paper, we describe a ....

A. Blum and A. Kalai. A Note on Learning from Multiple-Instance Examples. To appear in Machine Learning, 1998.

....PAC learnable algorithm. Specifically, they showed that if there is an algorithm to e#ciently PAClearn from multiple instance examples that are distributed arbitrarily, then it is also possible to e#ciently PAC learn DNF formulas. It is generally assumed that PAClearning DNF formulas is hard. Blum and Kalai, 1998 ] a#rm Auer s proofs. In addition, they show that learning from multiple instance examples can be reduced to learning with one sided noise, twosided noise, and learning in the statistical query model [ Kearns and Vazirani, 1994 ] They also slightly improve Auer et al. s sample bounds to O( d ....

A. Blum and A. Kalai. A note on learning from multipleinstance examples. To appear in Machine Learning, 1998.

....bag (the noise ratio) can be arbitrarily high. The multiple instance learning model was only recently formalized by [ Dietterich et al. 1997 ] where they develop algorithms for the drug activity prediction problem. This work was followed by [ Long and Tan, 1996, Auer et al. 1996, Blum and Kalai, 1998 ] who showed that it is difficult to PAC learn in the Multiple Instance model unless very restrictive independence assumptions are made about the way in which examples are generated. Auer, 1997 ] shows that despite these assumptions, the MULTINST algorithm performs competitively on the drug ....

A. Blum and A. Kalai. A Note on Learning from Multiple-Instance Examples. To appear in Machine Learning, 1998.

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Avrim Blum and Adam Kalai. A Note on Learning from Multiple-Instance Examples. Machine Learning, 30:23--29, 1998.

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A. Blum and A. Kalai. A note on learning from multiple-instance examples. Machine Learning, vol.30, no.1, pp.23--29, 1998.

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Blum, A., Kalai, A.: A note on learning from multiple-instance examples. Machine Learning 30 (1998) 23--29

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A. Blum and A. Kalai. A note on learning from multiple-instance examples. Machine Learning, vol.30, no.1, pp.23--29, 1998.

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Blum, A., Kalai, A.: A note on learning from multiple-instance examples. Machine Learning 30 (1998) 23--29

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