Statistical Comparisons of Classifiers over Multiple Data Sets

Introduction Over the last years, the machine learning community has become increasingly aware of the need for ... friendly graphs. In Section 4 we shall provide some empirical insights into the properties of the tests. 2. Previous Work ... random resampling 11 29 44 32 54 separate subset 5 11 0 13 9 Score function [%] classification accuracy ...

  Introduction, Graph, Random

A Neural Probabilistic Language Model

Journal of Machine Learning Research 3 (2003) 1137–1155 Submitted 4/02; Published 2/03 A Neural Probabilistic Language Model Yoshua Bengio BENGIOY@IRO.UMONTREAL.

  Language, Model, Language model

Latent Dirichlet Allocation

LATENT DIRICHLET ALLOCATION This line of thinking leads to the latent Dirichlet allocation (LDA) model that we present in the current paper. It is important to emphasize that an assumption of exchangeability is not equivalent to an as-

  Paper, Talent, Allocation, Latent dirichlet allocation, Dirichlet

ExploringtheLimitsofTransferLearningwithaUnified Text-to ...

Raffel, Shazeer, Roberts, Lee, Narang, Matena, Zhou, Li and Liu ormeaningofwords)tohigh-level(e.g.thatatubaistoolargetofitinmostbackpacks). In modern machine ...

  Narang

A Kernel Two-Sample Test

Kolmogorov-Smirnov and Earth-Mover’s distances, which are based ondifferent function classes; collectively these are known as integral probability metrics (Muller, 1997). On a more practical¨ note, the MMD has a reasonable computational cost, when compared with …

  Tests, Samples, Kolmogorov, Two sample test

Journal of Mac hine Learning Researc h 1 (2001) 211{244 ...

tro duction In sup ervise d le arning w e are giv en a set of examples input v ectors f x n g N n =1 along with corresp onding targets f t n g N n =1, the latter of whic h migh be real v alues (in r e gr ession) or class lab els (classi c ation). F rom this `training' set w e wish to learn a mo del of the dep endency of the targets on the ...

  Conduit, Tro duction

Visualizing Data using t-SNE

VISUALIZING DATA USING T-SNE 2. Stochastic Neighbor Embedding Stochastic Neighbor Embedding (SNE) starts by converting the high-dimensional Euclidean dis-tances between datapoints into conditional probabilities that represent similarities.1 The similarity of datapoint xj to datapoint xi is the conditional probability, pjji, that xi would pick xj as its neighbor

  Ent s

Latent Dirichlet Allocation

discrete data such as text corpora. LDA is a three-level hierarchical Bayesian model, in which each item of a collection is modeled as a finite mixture over an underlying set of topics. Each topic is, in turn, modeled as an infinite mixture over …

  Topics, Talent, Allocation, Hierarchical, Latent dirichlet allocation, Dirichlet

Dropout: A Simple Way to Prevent Neural Networks from …

probability pduring training, the outgoing weights of that unit are multiplied by pat test time as shown in Figure 2. This ensures that for any hidden unit the expected output (under the distribution used to drop units at training time) is the same as the actual output at test time.

  Form, Network, Distribution, Prevent, Probability, Neural, To prevent neural networks from

Scikit-learn: Machine Learning in Python

Cython: a language for combining C in Python. Cython makes it easy to reach the performance of compiled languages with Python-like syntax and high-level operations. It is also used to bind compiled libraries, eliminating the boilerplate code of Python/C extensions. 4. Code Design Objects specified by interface, not by inheritance.

  Python, Machine, Learning, Machine learning, Syntax, Of python

Qualitative Research Designs - University of Tennessee

from Cortazzi (1993) and Riessman (1993); and quantitative (e.g., statisti-cal stories in event-history modeling) and qualitative approaches in social research from Elliott (2005). The annual series Narrative Study of Lives that began in 1993 (e.g., Josselson & Lieblich, 1993) and the journal Narrative

  Research, Qualitative, Qualitative research, Sta tistical, Statisti

ORIGINAL RESEARCH Fast-Food Consumption and Obesity …

Research Triangle Park, North Carolina) for all statisti-cal analyses to account for the complex survey design and analysis weights that adjusted for the probability of selec-tion and poststratified to the Michigan adult population by age, race, and sex. We calculated prevalence estimates and 95% confidence intervals (CIs) for regular fast-food

  Sta tistical, Statisti

Statistical Education of Teachers

The GAISE report also breaks down the statisti-cal problem-solving process into four components: formulate questions (clarify the problem at hand and formulate questions that can be answered with data), collect data (design and employ a plan to collect appro-priate data), analyze data (select and use appropriate

  Education, Statistical, Teacher, Sta tistical, Statistical education of teachers, Statisti

A Student's Guide to R

4 horton, kaplan, pruim 10 More than Two Variables 75 11 Probability Distributions & Random Variables 83 12 Power Calculations 89 13 Data Management 93 14 Health Evaluation (HELP) Study 107 15 Exercises and Problems 111 16 Bibliography 115 17 Index 117