title: Statistical Learning in Multiple Instance Problems

authors: Xu, Xin

subjects: machine learning

abstract: Multiple instance (MI) learning is a relatively new topic in machine learning.  It is concerned with supervised learning but differs from normal supervised learning in two points: (1) it has multiple instances in an example (and there is only one instance in an example in standard supervised learning), and (2) only one class label is observable for all the instances in an example (whereas each instance has its own class label in normal supervised learning).  In MI learning there is a common assumption regarding the relationship between the class label of an example and the ``unobservable'' class labels of the instances inside it.  This assumption, which is called the ``MI assumption'' in this thesis, states that ``An example is positive if at least one of its instances is positive and negative otherwise''.  

In this thesis, we first categorize current MI methods into a new framework.  According to our analysis, there are two main categories of MI methods, instance-based and metadata-based approaches.  Then we propose a new assumption for MI learning, called the ``collective assumption''.  Although this assumption has been used in some previous MI methods, it has never been explicitly stated,\footnote{As a matter of fact, for some of these methods, it is actually claimed that they use the standard MI assumption stated above.} and this is the first time that it is formally specified.  Using this new assumption we develop new algorithms --- more specifically two instance-based and one metadata-based methods.  All of these methods build probabilistic models and thus implement statistical learning algorithms.  The exact generative models underlying these methods are explicitly stated and illustrated so that one may clearly understand the situations to which they can best be applied.  The empirical results presented in this thesis show that they are competitive on standard benchmark datasets.  Finally, we explore some practical applications of MI learning, both existing and new ones.

This thesis makes three contributions: a new framework for MI learning, new MI methods based on this framework and experimental results for new applications of MI learning.

text: Statistical Learning in Multiple Instance Problems Multiple instance (MI) learning is a relatively new topic in machine learning.  It is concerned with supervised learning but differs from normal supervised learning in two points: (1) it has multiple instances in an example (and there is only one instance in an example in standard supervised learning), and (2) only one class label is observable for all the instances in an example (whereas each instance has its own class label in normal supervised learning).  In MI learning there is a common assumption regarding the relationship between the class label of an example and the ``unobservable'' class labels of the instances inside it.  This assumption, which is called the ``MI assumption'' in this thesis, states that ``An example is positive if at least one of its instances is positive and negative otherwise''.  

In this thesis, we first categorize current MI methods into a new framework.  According to our analysis, there are two main categories of MI methods, instance-based and metadata-based approaches.  Then we propose a new assumption for MI learning, called the ``collective assumption''.  Although this assumption has been used in some previous MI methods, it has never been explicitly stated,\footnote{As a matter of fact, for some of these methods, it is actually claimed that they use the standard MI assumption stated above.} and this is the first time that it is formally specified.  Using this new assumption we develop new algorithms --- more specifically two instance-based and one metadata-based methods.  All of these methods build probabilistic models and thus implement statistical learning algorithms.  The exact generative models underlying these methods are explicitly stated and illustrated so that one may clearly understand the situations to which they can best be applied.  The empirical results presented in this thesis show that they are competitive on standard benchmark datasets.  Finally, we explore some practical applications of MI learning, both existing and new ones.

This thesis makes three contributions: a new framework for MI learning, new MI methods based on this framework and experimental results for new applications of MI learning.

year: 2003

title: An exact solution for magnetic annihilation in a curved current sheet

authors: Litvinenko, Yuri E.

subjects: magnetic reconnection

abstract: An exact magnetohydrodynamic solution is presented for steady magnetic annihilation (merging) in an incompressible resistive viscous plasma. The merging, driven by an axisymmetric stagnation flow on a cylinder, takes place in a curved current sheet that is perpendicular to the plane in which the plasma flow stagnates. The new solution extends earlier models of flux pileup merging in a flat current sheet, driven by stagnation-point flows. The new solution remains valid in the presence of both the isotropic and anisotropic (parallel) plasma viscosity. The geometry of the solution may make it useful in modeling the photospheric flux cancellation on the Sun.

text: An exact solution for magnetic annihilation in a curved current sheet An exact magnetohydrodynamic solution is presented for steady magnetic annihilation (merging) in an incompressible resistive viscous plasma. The merging, driven by an axisymmetric stagnation flow on a cylinder, takes place in a curved current sheet that is perpendicular to the plane in which the plasma flow stagnates. The new solution extends earlier models of flux pileup merging in a flat current sheet, driven by stagnation-point flows. The new solution remains valid in the presence of both the isotropic and anisotropic (parallel) plasma viscosity. The geometry of the solution may make it useful in modeling the photospheric flux cancellation on the Sun.

year: 2013

title: “Literacy is not a Māori thing": Debunking the deficit discourse

authors: Derby, Melissa

abstract: Literacy has been entrenched in Māori society for more than 200 years, meeting needs that range from serving a political purpose to reading purely for pleasure. Literacy is not solely a Western competency, and Māoristriving for excellence in literacy is not an act of modern-day colonisation. Instead, what the evidence shows is that literacy is a skill at which Māori have excelled, and can excel, without compromising their identity asMāori. I challenge us to be conscious of the discourses underpinning statements related to Māori learners, however well-meaning those statements might be, and reiterate that literacy is indeed “a Māori thing” – both historically and in contemporary times.

text: “Literacy is not a Māori thing": Debunking the deficit discourse Literacy has been entrenched in Māori society for more than 200 years, meeting needs that range from serving a political purpose to reading purely for pleasure. Literacy is not solely a Western competency, and Māoristriving for excellence in literacy is not an act of modern-day colonisation. Instead, what the evidence shows is that literacy is a skill at which Māori have excelled, and can excel, without compromising their identity asMāori. I challenge us to be conscious of the discourses underpinning statements related to Māori learners, however well-meaning those statements might be, and reiterate that literacy is indeed “a Māori thing” – both historically and in contemporary times.

year: 2022-03-22