Multiple Instance Learning: A Survey of Problem Characteristics and Applications

Multiple instance learning (MIL) is a form of weakly supervised learning where training instances are arranged in sets, called bags, and a label is provided for the entire bag. This formulation is gaining interest because it naturally fits various problems and allows to leverage weakly labeled data. Consequently, it has been used in diverse application fields such as computer vision and document classification. However, learning from bags raises important challenges that are unique to MIL. This paper provides a comprehensive survey of the characteristics which define and differentiate the types of MIL problems. Until now, these problem characteristics have not been formally identified and described. As a result, the variations in performance of MIL algorithms from one data set to another are difficult to explain. In this paper, MIL problem characteristics are grouped into four broad categories: the composition of the bags, the types of data distribution, the ambiguity of instance labels, and the task to be performed. Methods specialized to address each category are reviewed. Then, the extent to which these characteristics manifest themselves in key MIL application areas are described. Finally, experiments are conducted to compare the performance of 16 state-of-the-art MIL methods on selected problem characteristics. This paper provides insight on how the problem characteristics affect MIL algorithms, recommendations for future benchmarking and promising avenues for research

Selective Subtraction: An Extension of Background Subtraction

Background subtraction or scene modeling techniques model the background of the scene using the stationarity property and classify the scene into two classes of foreground and background. In doing so, most moving objects become foreground indiscriminately, except for perhaps some waving tree leaves, water ripples, or a water fountain, which are typically learned as part of the background using a large training set of video data. Traditional techniques exhibit a number of limitations including inability to model partial background or subtract partial foreground, inflexibility of the model being used, need for large training data and computational inefficiency. In this thesis, we present our work to address each of these limitations and propose algorithms in two major areas of research within background subtraction namely single-view and multi-view based techniques. We first propose the use of both spatial and temporal properties to model a dynamic scene and show how Mapping Convergence framework within Support Vector Mapping Convergence (SVMC) can be used to minimize training data. We also introduce a novel concept of background as the objects other than the foreground, which may include moving objects in the scene that cannot be learned from a training set because they occur only irregularly and sporadically, e.g. a walking person. We propose a selective subtraction method as an alternative to standard background subtraction, and show that a reference plane in a scene viewed by two cameras can be used as the decision boundary between foreground and background. In our definition, the foreground may actually occur behind a moving object. Our novel use of projective depth as a decision boundary allows us to extend the traditional definition of background subtraction and propose a much more powerful framework. Furthermore, we show that the reference plane can be selected in a very flexible manner, using for example the actual moving objects in the scene, if needed. We present diverse set of examples to show that: (i) the technique performs better than standard background subtraction techniques without the need for training, camera calibration, disparity map estimation, or special camera configurations; (ii) it is potentially more powerful than standard methods because of its flexibility of making it possible to select in real-time what to filter out as background, regardless of whether the object is moving or not, or whether it is a rare event or a frequent one; (iii) the technique can be used for a variety of situations including when images are captured using stationary cameras or hand-held cameras and for both indoor and outdoor scenes. We provide extensive results to show the effectiveness of the proposed framework in a variety of very challenging environments

Intelligent Data Mining using Kernel Functions and Information Criteria

Radial Basis Function (RBF) Neural Networks and Support Vector Machines (SVM) are two powerful kernel related intelligent data mining techniques. The current major problems with these methods are over-fitting and the existence of too many free parameters. The way to select the parameters can directly affect the generalization performance(test error) of theses models. Current practice in how to choose the model parameters is an art, rather than a science in this research area. Often, some parameters are predetermined, or randomly chosen. Other parameters are selected through repeated experiments that are time consuming, costly, and computationally very intensive. In this dissertation, we provide a two-stage analytical hybrid-training algorithm by building a bridge among regression tree, EM algorithm, and Radial Basis Function Neural Networks together. Information Complexity (ICOMP) criterion of Bozdogan along with other information based criteria are introduced and applied to control the model complexity, and to decide the optimal number of kernel functions. In the first stage of the hybrid, regression tree and EM algorithm are used to determine the kernel function parameters. In the second stage of the hybrid, the weights (coefficients) are calculated and information criteria are scored. Kernel Principal Component Analysis (KPCA) using EM algorithm for feature selection and data preprocessing is also introduced and studied. Adaptive Support Vector Machines (ASVM) and some efficient algorithms are given to deal with massive data sets in support vector classifications. Versatility and efficiency of the new proposed approaches are studied on real data sets and via Monte Carlo sim- ulation experiments

Gaussian Processes for Text Regression

Text Regression is the task of modelling and predicting numerical indicators or response variables from textual data. It arises in a range of different problems, from sentiment and emotion analysis to text-based forecasting. Most models in the literature apply simple text representations such as bag-of-words and predict response variables in the form of point estimates. These simplifying assumptions ignore important information coming from the data such as the underlying uncertainty present in the outputs and the linguistic structure in the textual inputs. The former is particularly important when the response variables come from human annotations while the latter can capture linguistic phenomena that go beyond simple lexical properties of a text. In this thesis our aim is to advance the state-of-the-art in Text Regression by improving these two aspects, better uncertainty modelling in the response variables and improved text representations. Our main workhorse to achieve these goals is Gaussian Processes (GPs), a Bayesian kernelised probabilistic framework. GP-based regression models the response variables as well-calibrated probability distributions, providing additional information in predictions which in turn can improve subsequent decision making. They also model the data using kernels, enabling richer representations based on similarity measures between texts. To be able to reach our main goals we propose new kernels for text which aim at capturing richer linguistic information. These kernels are then parameterised and learned from the data using efficient model selection procedures that are enabled by the GP framework. Finally we also capitalise on recent advances in the GP literature to better capture uncertainty in the response variables, such as multi-task learning and models that can incorporate non-Gaussian variables through the use of warping functions. Our proposed architectures are benchmarked in two Text Regression applications: Emotion Analysis and Machine Translation Quality Estimation. Overall we are able to obtain better results compared to baselines while also providing uncertainty estimates for predictions in the form of posterior distributions. Furthermore we show how these models can be probed to obtain insights about the relation between the data and the response variables and also how to apply predictive distributions in subsequent decision making procedures

Modelling Instrumental Gestures and Techniques: A Case Study of Piano Pedalling

PhD ThesisIn this thesis we propose a bottom-up approach for modelling instrumental gestures and techniques, using piano pedalling as a case study. Pedalling gestures play a vital role in expressive piano performance. They can be categorised into di erent pedalling techniques. We propose several methods for the indirect acquisition of sustain-pedal techniques using audio signal analyses, complemented by the direct measurement of gestures with sensors. A novel measurement system is rst developed to synchronously collect pedalling gestures and piano sound. Recognition of pedalling techniques starts by using the gesture data. This yields high accuracy and facilitates the construction of a ground truth dataset for evaluating the audio-based pedalling detection algorithms. Studies in the audio domain rely on the knowledge of piano acoustics and physics. New audio features are designed through the analysis of isolated notes with di erent pedal e ects. The features associated with a measure of sympathetic resonance are used together with a machine learning classi er to detect the presence of legato-pedal onset in the recordings from a speci c piano. To generalise the detection, deep learning methods are proposed and investigated. Deep Neural Networks are trained using a large synthesised dataset obtained through a physical-modelling synthesiser for feature learning. Trained models serve as feature extractors for frame-wise sustain-pedal detection from acoustic piano recordings in a proposed transfer learning framework. Overall, this thesis demonstrates that recognising sustain-pedal techniques is possible to a high degree of accuracy using sensors and also from audio recordings alone. As the rst study that undertakes pedalling technique detection in real-world piano performance, it complements piano transcription methods. Moreover, the underlying relations between pedalling gestures, piano acoustics and audio features are identi ed. The varying e ectiveness of the presented features and models can also be explained by di erences in pedal use between composers and musical eras

Kernel Methods and Measures for Classification with Transparency, Interpretability and Accuracy in Health Care

Support vector machines are a popular method in machine learning. They learn from data about a subject, for example, lung tumors in a set of patients, to classify new data, such as, a new patient’s tumor. The new tumor is classified as either cancerous or benign, depending on how similar it is to the tumors of other patients in those two classes—where similarity is judged by a kernel. The adoption and use of support vector machines in health care, however, is inhibited by a perceived and actual lack of rationale, understanding and transparency for how they work and how to interpret information and results from them. For example, a user must select the kernel, or similarity function, to be used, and there are many kernels to choose from but little to no useful guidance on choosing one. The primary goal of this thesis is to create accurate, transparent and interpretable kernels with rationale to select them for classification in health care using SVM—and to do so within a theoretical framework that advances rationale, understanding and transparency for kernel/model selection with atomic data types. The kernels and framework necessarily co-exist. The secondary goal of this thesis is to quantitatively measure model interpretability for kernel/model selection and identify the types of interpretable information which are available from different models for interpretation. Testing my framework and transparent kernels with empirical data I achieve classification accuracy that is better than or equivalent to the Gaussian RBF kernels. I also validate some of the model interpretability measures I propose

Coopération de réseaux de caméras ambiantes et de vision embarquée sur robot mobile pour la surveillance de lieux publics

Actuellement, il y a une demande croissante pour le déploiement de robots mobile dans des lieux publics. Pour alimenter cette demande, plusieurs chercheurs ont déployé des systèmes robotiques de prototypes dans des lieux publics comme les hôpitaux, les supermarchés, les musées, et les environnements de bureau. Une principale préoccupation qui ne doit pas être négligé, comme des robots sortent de leur milieu industriel isolé et commencent à interagir avec les humains dans un espace de travail partagé, est une interaction sécuritaire. Pour un robot mobile à avoir un comportement interactif sécuritaire et acceptable - il a besoin de connaître la présence, la localisation et les mouvements de population à mieux comprendre et anticiper leurs intentions et leurs actions. Cette thèse vise à apporter une contribution dans ce sens en mettant l'accent sur les modalités de perception pour détecter et suivre les personnes à proximité d'un robot mobile. Comme une première contribution, cette thèse présente un système automatisé de détection des personnes visuel optimisé qui prend explicitement la demande de calcul prévue sur le robot en considération. Différentes expériences comparatives sont menées pour mettre clairement en évidence les améliorations de ce détecteur apporte à la table, y compris ses effets sur la réactivité du robot lors de missions en ligne. Dans un deuxiè contribution, la thèse propose et valide un cadre de coopération pour fusionner des informations depuis des caméras ambiant affixé au mur et de capteurs montés sur le robot mobile afin de mieux suivre les personnes dans le voisinage. La même structure est également validée par des données de fusion à partir des différents capteurs sur le robot mobile au cours de l'absence de perception externe. Enfin, nous démontrons les améliorations apportées par les modalités perceptives développés en les déployant sur notre plate-forme robotique et illustrant la capacité du robot à percevoir les gens dans les lieux publics supposés et respecter leur espace personnel pendant la navigation.This thesis deals with detection and tracking of people in a surveilled public place. It proposes to include a mobile robot in classical surveillance systems that are based on environment fixed sensors. The mobile robot brings about two important benefits: (1) it acts as a mobile sensor with perception capabilities, and (2) it can be used as means of action for service provision. In this context, as a first contribution, it presents an optimized visual people detector based on Binary Integer Programming that explicitly takes the computational demand stipulated into consideration. A set of homogeneous and heterogeneous pool of features are investigated under this framework, thoroughly tested and compared with the state-of-the-art detectors. The experimental results clearly highlight the improvements the different detectors learned with this framework bring to the table including its effect on the robot's reactivity during on-line missions. As a second contribution, the thesis proposes and validates a cooperative framework to fuse information from wall mounted cameras and sensors on the mobile robot to better track people in the vicinity. Finally, we demonstrate the improvements brought by the developed perceptual modalities by deploying them on our robotic platform and illustrating the robot's ability to perceive people in supposed public areas and respect their personal space during navigation

Äriprotsesside ajaliste näitajate selgitatav ennustav jälgimine

Kaasaegsed ettevõtte infosüsteemid võimaldavad ettevõtetel koguda detailset informatsiooni äriprotsesside täitmiste kohta. Eelnev koos masinõppe meetoditega võimaldab kasutada andmejuhitavaid ja ennustatavaid lähenemisi äriprotsesside jõudluse jälgimiseks. Kasutades ennustuslike äriprotsesside jälgimise tehnikaid on võimalik jõudluse probleeme ennustada ning soovimatu tegurite mõju ennetavalt leevendada. Tüüpilised küsimused, millega tegeleb ennustuslik protsesside jälgimine on “millal antud äriprotsess lõppeb?” või “mis on kõige tõenäolisem järgmine sündmus antud äriprotsessi jaoks?”. Suurim osa olemasolevatest lahendustest eelistavad täpsust selgitatavusele. Praktikas, selgitatavus on ennustatavate tehnikate tähtis tunnus. Ennustused, kas protsessi täitmine ebaõnnestub või selle täitmisel võivad tekkida raskused, pole piisavad. On oluline kasutajatele seletada, kuidas on selline ennustuse tulemus saavutatud ning mida saab teha soovimatu tulemuse ennetamiseks. Töö pakub välja kaks meetodit ennustatavate mudelite konstrueerimiseks, mis võimaldavad jälgida äriprotsesse ning keskenduvad selgitatavusel. Seda saavutatakse ennustuse lahtivõtmisega elementaarosadeks. Näiteks, kui ennustatakse, et äriprotsessi lõpuni on jäänud aega 20 tundi, siis saame anda seletust, et see aeg on moodustatud kõikide seni käsitlemata tegevuste lõpetamiseks vajalikust ajast. Töös võrreldakse omavahel eelmainitud meetodeid, käsitledes äriprotsesse erinevatest valdkondadest. Hindamine toob esile erinevusi selgitatava ja täpsusele põhinevale lähenemiste vahel. Töö teaduslik panus on ennustuslikuks protsesside jälgimiseks vabavaralise tööriista arendamine. Süsteemi nimeks on Nirdizati ning see süsteem võimaldab treenida ennustuslike masinõppe mudeleid, kasutades nii töös kirjeldatud meetodeid kui ka kolmanda osapoole meetodeid. Hiljem saab treenitud mudeleid kasutada hetkel käivate äriprotsesside tulemuste ennustamiseks, mis saab aidata kasutajaid reaalajas.Modern enterprise systems collect detailed data about the execution of the business processes they support. The widespread availability of such data in companies, coupled with advances in machine learning, have led to the emergence of data-driven and predictive approaches to monitor the performance of business processes. By using such predictive process monitoring approaches, potential performance issues can be anticipated and proactively mitigated. Various approaches have been proposed to address typical predictive process monitoring questions, such as what is the most likely continuation of an ongoing process instance, or when it will finish. However, most existing approaches prioritize accuracy over explainability. Yet in practice, explainability is a critical property of predictive methods. It is not enough to accurately predict that a running process instance will end up in an undesired outcome. It is also important for users to understand why this prediction is made and what can be done to prevent this undesired outcome. This thesis proposes two methods to build predictive models to monitor business processes in an explainable manner. This is achieved by decomposing a prediction into its elementary components. For example, to explain that the remaining execution time of a process execution is predicted to be 20 hours, we decompose this prediction into the predicted execution time of each activity that has not yet been executed. We evaluate the proposed methods against each other and various state-of-the-art baselines using a range of business processes from multiple domains. The evaluation reaffirms a fundamental trade-off between explainability and accuracy of predictions. The research contributions of the thesis have been consolidated into an open-source tool for predictive business process monitoring, namely Nirdizati. It can be used to train predictive models using the methods described in this thesis, as well as third-party methods. These models are then used to make predictions for ongoing process instances; thus, the tool can also support users at runtime