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URI: http://hdl.handle.net/2144/905

As Boston University's largest academic division, the College and Graduate School of Arts & Sciences is the heart of the BU experience. Undergraduates choose from more than 2,500 courses in the humanities, natural and social sciences, and mathematics and computer science and pursue BA programs in more than 70 concentrations. Graduate students can earn an MA or PhD in nearly 50 fields of the humanities; the natural, social, and mathematical sciences; theology; and music. A robust and productive learning environment awaits everyone who enrolls here.

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  • Learning translations via images with a massively multilingual image dataset 

    Hewitt, John; Ippolito, Daphne; Callahan, Brendan; Kriz, Reno; Wijaya, Derry; Callison-Burch, Chris (Association for Computational Linguistics, 2018-07-15)
    We conduct the most comprehensive study to date into translating words via images. To facilitate research on the task, we introduce a large-scale multilingual corpus of images, each labeled with the word it represents. ...
  • On some integrated approaches to inference 

    Kon, Mark A.; Plaskota, Leszek (2012)
    We present arguments for the formulation of unified approach to different standard continuous inference methods from partial information. It is claimed that an explicit partition of information into a priori (prior knowledge) ...
  • Empirical normalization for quadratic discriminant analysis and classifying cancer subtypes 

    Kon, Mark A.; Nikolaev, Nikolay (IEEE, 2011-12)
    We introduce a new discriminant analysis method (Empirical Discriminant Analysis or EDA) for binary classification in machine learning. Given a dataset of feature vectors, this method defines an empirical feature map ...
  • On the probabilistic continuous complexity conjecture 

    Kon, Mark A. (2012)
    In this paper we prove the probabilistic continuous complexity conjecture. In continuous complexity theory, this states that the complexity of solving a continuous problem with probability approaching 1 converges (in this ...
  • Feature vector regularization in machine learning 

    Fan, Yue; Raphael, Louise; Kon, Mark (2012-12-19)
    Problems in machine learning (ML) can involve noisy input data, and ML classification methods have reached limiting accuracies when based on standard ML data sets consisting of feature vectors and their classes. Greater ...
  • Transcription factor-DNA binding via machine learning ensembles 

    Kon, Mark A.; Delisi, Charles; Fan, Yue (2018-05-27)
    The network of interactions between transcription factors (TFs) and their regulatory gene targets governs many of the behaviors and responses of cells. Construction of a transcriptional regulatory network involves three ...
  • Relationships among interpolation bases of wavelet spaces and approximation spaces 

    Zhang, Zhiguo; Kon, Mark A. (2012-12-28)
    A multiresolution analysis is a nested chain of related approximation spaces.This nesting in turn implies relationships among interpolation bases in the approximation spaces and their derived wavelet spaces. Using these ...
  • A complexity analysis of statistical learning algorithms 

    Kon, Mark A. (2012-12-19)
    We apply information-based complexity analysis to support vector machine (SVM) algorithms, with the goal of a comprehensive continuous algorithmic analysis of such algorithms. This involves complexity measures in which ...
  • Pathway-based classification of cancer subtypes 

    Kim, Shinuk; Kon, Mark; DeLisi, Charles (BIOMED CENTRAL LTD, 2012-07-03)
    BACKGROUND: Molecular markers based on gene expression profiles have been used in experimental and clinical settings to distinguish cancerous tumors in stage, grade, survival time, metastasis, and drug sensitivity. ...
  • Top scoring pairs for feature selection in machine learning and applications to cancer outcome prediction 

    Shi, Ping; Ray, Surajit; Zhu, Qifu; Kon, Mark A. (BIOMED CENTRAL LTD, 2011-09-23)
    BACKGROUND: The widely used k top scoring pair (k-TSP) algorithm is a simple yet powerful parameter-free classifier. It owes its success in many cancer microarray datasets to an effective feature selection algorithm ...

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