In pattern recognition, &ldquo / bias-variance&rdquo / trade-off is a challenging issue that the scientists has been working to get better generalization performances over the last decades. Among many learning methods, two-layered homogeneous stacked generalization has been reported to be successful in the literature, in different problem domains such as object recognition and image annotation. The aim of this work is two-folded. First, the problems of stacked generalization are attacked by a proposed novel architecture. Then, a set of success criteria for stacked generalization is studied. A serious drawback of stacked generalization architecture is the sensitivity to curse of dimensionality problem. In order to solve this problem, a new architecture named &ldquo / unanimous decision&rdquo / is designed. The performance of this architecture is shown to be comparably similar to two layered homogeneous stacked generalization architecture in low number of classes while it performs better than stacked generalization architecture in higher number of classes. Additionally, a new success criterion for two layered homogeneous stacked generalization architecture is proposed based on the individual properties of the used descriptors and it is verified in synthetic datasets.

Stacked Generalization (SG) is an ensemble learning technique, which aims to increase the performance of individual classifiers by combining them under a hierarchical architecture. This study consists of two major parts. In the first part, the performance of Stacked Generalization technique is analyzed with respect to the performance of the individual classifiers and the content of the training data. In the second part, based on the findings for a new class of algorithms, called Meta-Fuzzified Yield Value (Meta-FYV) is introduced. The first part introduces and verifies two hypotheses by a set of controlled experiments to assure the performance gain for SG. The learning mechanisms of SG to achieve high performance are explored and the relationship between the performance of the individual classifiers and that of SG is investigated. It is shown that if the samples in the training set are correctly classified by at least one base layer classifier, then, the generalization performance of the SG is increased, compared to the performance of the individual classifiers. In the second hypothesis, the effect of the spurious samples, which are not correctly labeled by any of the base layer classifiers, is investigated. In the second part of the thesis, six theorems are constructed based on the analysis of the feature spaces and the stacked generalization architecture. Based on the theorems and hypothesis, a new class of SG algorithms is proposed. The experiments are performed on both Corel data and synthetically generated data, using parallel programming techniques, on a high performance cluster.

Thesis (M.A.)--University of California, San Diego, 2008. / Accompanying disc is DVD-ROM, and contains sound files for recordings of 2nd and 3rd compositions. Title from first page of PDF file (viewed June 25, 2008). Available via ProQuest Digital Dissertations. Reproduced from holograph.

Thesis (D.M.A.)--University of North Texas, 2001. / For solo trombone, 4 trumpets (2 doubling flugelhorn), 4 horns, 3 trombones, euphonium and tuba. Includes analysis by composer. Includes bibliographical references.

Thesis (Ph. D.)--University of California, San Diego, 2003. / Vita. "Three related compositions written for string quartet, small ensemble (soprano, violin, viola, cello, flute, clarinet, piano) and percussion duo"--P. viii; 3rd work an open form composition. Also available on the World Wide Web. (Access restricted to UC campuses).

Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) of the breast is a widely used non-invasive approach to gather information about the underlying physiology of breast tumors. Recent studies indicate that breast tumor heterogeneity may reflect the presence of different levels of cellular aggressiveness or habitats within the tumor. This heterogeneity has been correlated to the variations in the contrast enhancement patterns within the tumor apparent on gadolinium-enhanced DCE-MRI. Although pathological and qualitative (based on contrast enhancement patterns) studies suggest the presence of clini- cal and molecular predictive tumor sub-regions, this has not been fully investigated in the quantitative domain. The new era of cancer imaging emphasizes the use of Radiomics to provide in vivo quan- titative prognostic and predictive imaging biomarkers. Thus Radiomics focuses on apply- ing image analysis techniques to quantify tumor radiographic properties to create mineable databases from radiological images. In this research work, the Radiomics approach was ap- plied to develop a novel computer aided diagnosis (CAD) model for quantifying intratumor heterogeneity not only within the tumor as a whole, but also within tumor habitats with an intent to build predictive models in breast cancer. The process of building these predictive models started with 2-D tumor segmentation followed by habitat extraction (based on vari- ations in contrast patterns and geometry) and textural kinetic feature extraction to quantify habitat heterogeneity. A new correlation based random subspace ensemble framework was developed to evaluate the textural kinetics from the individual tumor habitats. This new CAD framework was applied to predict two clinical and prognostic factors: Axillary lymph node (ALN) metastases and Estrogen receptor (ER) status. An AUC of more than 0.8 was achieved for classifying breast tumors based on number of ALN involvement. The highest AUC of 0.91 was achieved for classifying tumors with no ALN metastases from tumors with 4 or more ALN metastases. For classifying tumors based on ER status the highest AUC of 0.87 was achieved. These results were acquired by utilizing the textural kinetic features from the tumor habitat with rapid delayed washout. The results presented in this work showed that the heterogeneity within the tumor habitats which showed rapid contrast washout in the delayed phase, correlated with aggressive cellular phenotypes. This work hypothesizes that successfully quantifying these prognostic factors will prove to be clinically significant as it can improve the diagnostic accuracy. This, in turn, will im- prove the breast cancer treatment paradigm by providing more tailored treatment regimens for aggressive tumors.

Prelude -- Ray of lights -- Convergencias -- One art -- Dreams. / text

Unsupervised models can provide supplementary soft constraints to help classify new, "target" data since similar instances in the target set are more likely to share the same class label. Such models can also help detect possible differences between training and target distributions, which is useful in applications where concept drift may take place, as in transfer learning settings. This contribution describes two general frameworks that take as input class membership estimates from existing classifiers learnt on previously encountered "source" data, as well as a set of cluster labels from a cluster ensemble operating solely on the target data to be classified, and yield a consensus labeling of the target data. One of the proposed frameworks admits a wide range of loss functions and classification/clustering methods and exploits properties of Bregman divergences in conjunction with Legendre duality to yield a principled and scalable approach. The other approach is built on probabilistic mixture models and provides additional flexibility of distributed computation that is useful when the target data cannot be gathered in a single place for privacy or security concerns. A variety of experiments show that the proposed frameworks can yield results substantially superior to those provided by popular transductive learning techniques or by naively applying classifiers learnt on the original task to the target data. / text

Epitaph for a darling lady : (2012) -- Limelight : (2013) -- Blueprints : (2013) -- In the primal garden : (2014) -- Sacred spaces : (2012). / text

I detta självständiga arbete utforskar jag, utifrån hermeneutiskt inriktad fenomenologi, mina processer i samband med komponerande och arrangerande för en septett bestående av elgitarr, elbas, trumset och fyra blåsare. Under två terminer förde jag loggbok över de olika moment där jag arbetade med denna kreativa process, moment som bestod av komponerande, lektioner med lärare samt repetition med ensemblen. Resultatet redovisas med hjälp av tematiseringarna Sökande efter ett kompositoriskt flöde, Frön som gror och växer samt Känslan av att hitta nya vägar. Dessa teman ställs i diskussionskapitlet mot tidigare forskning på området samt hermeneutiskt fenomenologiskt perspektiv. / I this thesis I have explored my processes when composing and arranging for an ensemble consisting of electric guitar, electric bass, drums and four wind instruments. During two semesters I kept a log over the different events in which I’ve been working with this creative process, events such as composing, lessons with teachers and rehearsals with the ensemble. The result is reported in the themes The search of a compositional flow, Seeds that germinate and grow and The feeling of finding new ways. These themes are in the last chapter discussed in relation to previous research on the subject and a hermeneutic phenomenological perspective.