A Fractional Programming Framework for Support Vector Machine-type Formulations

We develop a theoretical framework for relating various formulations of regularization problems through fractional programming. We focus on problems with objective functions of the type L + λ · P , where the parameter λ lacks intuitive interpretation. We observe that fractional programming is an elegant approach to obtain bounds on the range of the parameter, and then generalize this approach to show that different forms can be obtained from a common fractional program. Furthermore, we apply the proposed framework in two concrete settings; we consider support vector machines (SVMs), where the framework clarifies the relation between various existing soft-margin dual forms for classification, and the SVM+ algorithm (Vapnik and Vashist, 2009), where we use this methodology to derive a new dual formulation, and obtain bounds on the cost parameter

Conic SMO

We derive an SMO-like algorithm for the optimization problem arising from the Second Order Cone Programming formulation of support vector machines (SVMs) for classification. Due to the square root term in the objective of the form, we cannot easily compute the location of the extrema using Newton‘s formula. Instead we modify the analytic solution proposed for SMO (Platt, 1999), to account for the particular objective function and partial derivatives. We then establish some theoretical properties of the algorithm based on the results of (Bordes et al., 2005), and develop practical working set selection similar to (Fan et al., 2005). In addition, we propose an efficient implementation, and compare the convergence rate of Conic-SMO and SMO on synthetic data

Data pre-processing for the preterm prediction study MFMU dataset

Preterm birth is a major public health problem with profound implications on society. There would be extreme value in being able to identify women at risk of preterm birth during the course of their pregnancy. Previous research has largely focused on individual risk factors correlated with preterm birth (e.g. prior preterm birth, race, and infection) and less on combining these factors in a way to understand the complex etiologies of preterm birth. We attempt to address this gap by conducting a deeper analysis of the preterm prediction study data collected by the NICHD Maternal Fetal Medicine Units (MFMU) Network, a high-quality data for over 3,000 singleton pregnancies having detailed study visits and biospecimen collection at 24, 26, 28 and 30 weeks gestation. Reports from this dataset used relatively straightforward biostatitistical methodologies such as relative risk assessments to measure associations between risk factors and PTB (Maternal Fetal Medicine Units Net- work. Biostatistical Coordinating Center NICHD Networks, 1995). These methods include descriptive statistics, Pearson correlation, Fisher’s exact tests and linear/logistic regression where risk factors are studied independent of each other. In order to perform detailed experiments on this data using non-linear Support Vector Machines and other machine learning (ML) methodologies, it is necessary to complete several pre-processing steps that we describe in this report

Sentiment Analysis of Twitter Data

We examine sentiment analysis on Twitter data. The contributions of this paper are: (1) We introduce POS-specific prior polarity features. (2) We explore the use of a tree kernel to obviate the need for tedious feature engineering. The new features (in conjunction with previously proposed features) and the tree kernel perform approximately at the same level, both outperforming the state-of-the-art baseline.

Classification of Discourse Functions of Affirmative Words in Spoken Dialogue: Presentation Powerpoint Slides

We present results of a series of machine learning experiments that address the classification of the discourse function of single affirmative cue words such as alright, okay and mm-hm in a spoken dialogue corpus. We suggest that a simple discourse/sentential distinction is not sufficient for such words and propose two additional classification sub-tasks: identifying (a) whether such words convey acknowledgment or agreement, and (b) whether they cue the beginning or end of a discourse segment. We also study the classification of each individual word into its most common discourse functions. We show that models based on contextual features extracted from the time-aligned transcripts approach the error rate of trained human aligners

Classification of Discourse Functions of Affirmative Words in Spoken Dialogue

We present results of a series of machine learning experiments that address the classification of the discourse function of single affirmative cue words such as alright, okay and mm-hm in a spoken dialogue corpus. We suggest that a simple discourse/sentential distinction is not sufficient for such words and propose two additional classification sub-tasks: identifying (a) whether such words convey acknowledgment or agreement, and (b) whether they cue the beginning or end of a discourse segment. We also study the classification of each individual word into its most common discourse functions. We show that models based on contextual features extracted from the time-aligned transcripts approach the error rate of trained human aligners