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Room 4192, Earth Sciences Building
Thu 25th August 2016
Yichen Zhao, UBC Statistics Master's student
Assessing performance of classifiers by cross-validation based on binary data
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In statistical applications, we are often asked to construct a classifier based on a random sample from a specific population. Once a classifier is built, we may use it to categorize new individuals from the population. The accuracy of categorizing new individuals is related to the precision of the classifier we built. Yet, the sample from the population is generally noisy. Unless the sample size is very large, the performance of the classifier in terms of correctly classifying new individuals is far from certain. In the data analysis stage, we usually look for the classifier that provides the highest success rate in classifying individuals in the given sample. This classifier's apparent rate of success generally over-estimates its precision when it is applied on new individuals from the population. To overcome this issue, the cross-validation technique is often suggested to be used to assess the performance of a classifier. In this project, we use simulation studies to investigate if the cross-validation technique indeed accurately estimates the performance of classifiers in various situations.

Room 4192, Earth Sciences Building (2207 Main Mall)
Tue 23rd August 2016
Camila Casquilho, UBC Statistics PhD Student
Models and monitoring designs for spatio-temporal climate data fields
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The modelling of temperature fields, which are crucial to understand a region's climate, can be challenging due to the topography of the study region. In the Pacific Northwest, extensive forests, mountains and proximity to the Pacific Ocean may create sudden changes in climate, contributing to the complexity of the modelling of temperature fields in this area. In this talk, we will firstly describe a modelling strategy for complex temperature fields that addresses non-stationarity via a new approach to modelling the spatial mean field.

Secondly, we will focus on the important task of surveillance of environmental processes. We will introduce a novel strategy for the design of monitoring networks where the goal is to choose a high-quality yet diverse set of locations. The idea is brought to this context via the theory of determinantal point processes (DPPs). We will demonstrate how DPPs, which have traditionally been used in other scientific domains, can also play an important role in statistical sciences, particularly in spatial design.

Time permitting, we will also discuss a recent challenge in spatial statistics applications: the data fusion problem. There has been an increased need for combining information from multiple sources that may have been observed on different spatial scales. We will give an overview of an ensemble modelling strategy which combines observed temperature measurements with outputs from an ensemble of deterministic climate models. This methodology can ultimately be used for calibration of model outputs, spatial mapping, and future forecasting.

ESB 4192- 2207 Main Mall
Fri 5th August 2016
Andreas Christmann
Department of Mathematics, University of Bayreuth, Germany
Robust Pairwise Learning With Kernels
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Abstract: Regularized empirical risk minimization plays an important role in machine learning theory. We will investigate a broad class of regularized pairwise learning (RPL) methods based on kernels. One example is regularized minimization of the error entropy loss which has recently attracted quite some interest from the viewpoint of consistency and learning rates. Another example is machine learning for ranking problems. We show that such RPL methods have additionally good statistical robustness properties, if the loss function and the kernel are chosen appropriately. We treat two cases of particular interest: (i) a bounded and non-convex loss function and (ii) an unbounded convex loss function satisfying a certain Lipschitz type condition. We will also give a result on the qualitative robustness of the empirical bootstrap of RPL methods. This is joint work with Prof. Dr. Ding-Xuan Zhou (City University of Hong Kong). The talk is based on a paper with the title ”Robustness of Regularized Pairwise Learning Methods Based on Kernels” which is accepted by the Journal of Complexity.

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Department of Statistics

Department of Statistics, University of British Columbia
3182 Earth Sciences Building
2207 Main Mall
Vancouver, BC, Canada V6T 1Z4
Tel: 604.822.0570
Fax: 604.822.6960

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