This thesis focuses on visualizations for machine learning tasks. More specifically, we create a taxonomy for existing machine learning visualizations, and design a system to help machine learning novices perform labelling ...

Music Information Retrieval (MIR) research tends to focus on audio features like melody and timbre of songs while largely ignoring lyrics. Lyrics and poetry adhere to a specific rhyme and meter structure which set them ...

Probabilistic Graphical Models are often used to e fficiently encode uncertainty in real world problems as probability distributions. Bayesian learning allows us to compute a posterior distribution over the parameters of ...

Probabilistic graphical models such as Bayesian networks and Markov networks provide a general framework to represent multivariate distributions while exploiting conditional independence. Over the years, many approaches ...

Within the field of multiagent systems, the area of computational social choice considers the problems arising when decisions must be made collectively by a group of agents. Usually such systems collect a ranking of the ...

The development of modern pipelined and multiple functional unit processors has increased the available instruction level parallelism. In order to fully utilize these resources, compiler writers spend large amounts of ...

Support vector machines (SVMs) have been a dominant machine learning technique for more than a decade. The intuitive principle behind SVM training is to find the maximum margin separating hyperplane for a given set of ...

Decision trees have been a popular machine learning technique for some time. Labelled data, examples each with a vector of values in a feature space, are used to create a structure that can assign a class to unseen examples ...

Many current test collections require the use of expert judgments during construction. The true label of each document is given by an expert assessor. However, the cost and effort associated with expert training and judging ...

Kernel methods have recently become popular in bioinformatics machine learning. Kernel methods allow linear algorithms to be applied to non-linear learning situations. By using kernels, non-linear learning problems can ...