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A Minimax Theorem with Applications to Machine Learning, Signal Processing, and Finance
convex optimization minimax theorem robust optimization
2015/7/9
This paper concerns a fractional function of the form x^Ta/sqrt{x^TBx}, where B is positive definite. We consider the game of choosing x from a convex set, to maximize the function, and choosing (a,B)...
Mean field variational Bayesian inference for support vector machine classification
Approximate Bayesian inference variable selection missing data mixed model Markov chain Monte Carlo
2013/6/14
A mean field variational Bayes approach to support vector machines (SVMs) using the latent variable representation on Polson & Scott (2012) is presented. This representation allows circumvention of ma...
Statistical Model Building, Machine Learning, and the Ah-Ha Moment
Statistical Model Building Machine Learning the Ah-Ha Moment
2013/4/28
The Committee of Presidents of Statistical Societies (COPSS) will celebrate its 50th Anniversary in 2013. As part of its celebration, COPSS intends to publish a book with contributions from the past r...
Relevance As a Metric for Evaluating Machine Learning Algorithms
Machine learning algorithms performance metric proba-bilistic approach
2013/4/28
In machine learning, the choice of a learning algorithm that is suitable for the application domain is critical. The performance metric used to compare different algorithms must also reflect the conce...
On Sparsity Inducing Regularization Methods for Machine Learning
Sparsity Inducing Regularization Methods for Machine Learning
2013/5/2
During the past years there has been an explosion of interest in learning methods based on sparsity regularization. In this paper, we discuss a general class of such methods, in which the regularizer ...
Machine Learning for Bioclimatic Modelling
Machine Learning Bioclimatic Modelling Geographic Range Artificial Neural Network Evolutionary Algorithm
2013/5/2
Many machine learning (ML) approaches are widely used to generate bioclimatic models for prediction of geographic range of organism as a function of climate. Applications such as prediction of range s...
Common Mistakes when Applying Computational Intelligence and Machine Learning to Stock Market modelling
Computational intelligence machine learning stock market equities automated stock tradin mistakes.
2012/9/17
For a number of reasons, computational intelligence and machine learning methods have been largely dismissed by the professional community. The reasons for this are numerous and ...
We describe a novel binary classification technique called Banded SVM (B-SVM). In the standard C-SVM formulation of Cortes et al. (1995), the decision rule is encouraged to lie in the interval [1, \in...
ProDiGe: PRioritization Of Disease Genes with multitask machine learning from positive and unlabeled examples
ProDiGe PRioritization Disease Genes multitask machine learning positive unlabeled examples
2011/7/6
Elucidating the genetic basis of human diseases is a central goal of genetics and molecular biology. While traditional linkage analysis and modern high-throughput techniques often provide long lists o...
Identifying Hosts of Families of Viruses: A Machine Learning Approach
viral host machine learning adaboost alternating decision tree mismatch k-mers
2011/6/21
Identifying viral pathogens and characterizing their transmission is essential to developing effective
public health measures in response to a pandemic. Phylogenetics, though currently the most popul...
Self-configuration from a Machine-Learning Perspective
Machine-Learning Perspective Self-configuration Sequen-tial Parameter Optimization
2011/6/21
The goal of machine learning is to provide solutions which are trained by data
or by experience coming from the environment. Many training algorithms exist and
some brilliant successes were achieved...
Nonsmooth Formulation of the Support Vector Machine for a Neural Decoding Problem
Optimization and Control (math.OC) Numerical Analysis (math.NA) Statistics Theory (math.ST)
2010/12/17
This paper formulates a generalized classification algorithm with an application to classifying (or `decoding') neural activity in the brain. Medical doctors and researchers have long been interested ...
Kernel methods in machine learning
Machine learning reproducing kernels support vector machines graphical models
2010/4/26
We review machine learning methods employing positive definite
kernels. These methods formulate learning and estimation problems
in a reproducing kernel Hilbert space (RKHS) of functions defined
on...
Support vector machine for functional data classification
Functional Data Analysis Support Vector Machine Classification Consistency
2010/4/29
In many applications, input data are sampled functions taking their values in infinite
dimensional spaces rather than standard vectors. This fact has complex consequences
on data analysis algorithms...
Machine Learning Department（ML）at Carnegie Mellon University（图）
ML Machine Learning Department at Carnegie Mellon University 统计学 机械学 计算机基础科学
2007/12/24
The Machine Learning Department is an academic department within Carnegie Mellon University's School of Computer Science. We focus on research and education in all areas of statistical machine learnin...