A Tutorial on Conformal Prediction

tically independent. This allows us to interpret “being right 95% of the time” in an unusually direct way. In §2.1, we illustrate this point with a well-worn example, normally distributed random vari-ables. In §2.2, we contrast confidence with full-fledged …

  Prediction, Begin, Conformal, Conformal prediction

Random Search for Hyper-Parameter Optimization

RANDOM SEARCH FOR HYPER-PARAMETER OPTIMIZATION search is used to identify regions in Λthat are promising and to develop the intuition necessary to choose the sets L(k).A major drawback of manual search is the difficulty in reproducing results.

  Optimization

Mathematical Writing CS209. Mathematical Writing—

Mathematical Writing by Donald E. Knuth, Tracy Larrabee, and Paul M. Roberts This report is based on a course of the same name given at Stanford University during autumn quarter, 1987. Here’s the catalog description: CS209. Mathematical Writing—Issues of technical writing and the ef-fective presentation of mathematics and computer science.

  Writing, Mathematical, Mathematical writing

Visualizing Data using t-SNE

VAN DER MAATEN AND HINTON a small qjji to model a large pjji), but there is only a small cost for using nearby map points to represent widely separated datapoints. This small cost comes from wasting some of the probability mass in the relevant Q distributions. In other words, the SNE cost function focuses on retaining the

  Der van

An Introduction to Variable and Feature Selection

outside of the scope of machine learning, so we focus here on building predictors. We consider in this section ranking criteria defined for individual variables, independe ntly of the context of others. Correlation methods belong to that category. We also limit ourselves to supervised learning criteria.

  Methods, Machine, Selection, Learning, Ranking, Variable, Machine learning

Scikit-learn: Machine Learning in Python

Keywords: Python, supervised learning, unsupervised learning, model selection 1. Introduction The Python programming language is establishing itself as one of the most popular languages for scientific computing. Thanks to its high-level interactive nature and its maturing ecosystem of sci-

  Python, Machine, Learning, Learn, Scikit, Scikit learn, Machine learning in python

Chapter 10 Error Detection and Correction

The Hamming distance between two word i th b f diffds is the number of differences between corresponding bits. 10.25. Example 10.4 Let us find the Hamming distance between two pairs of words. 1. The Hamming distance d(000, 011) is2because 2. The HiHamming distance d(10101, 11110) is 3 because

  Distance, Hamming, Hamming distance

Tutorial: Checksum and CRC Data Integrity Techniques for ...

– Hamming Distance 2 (HD=2); some two-bit errors are undetected. 18 Fletcher Checksum • Use two running one’s complement checksums – For fair comparison, each running sum is half width – E.g., 16-bit Fletcher Checksum is two 8-bit running sums – Initialize: A = 0; B = 0;

  Distance, Hamming, Hamming distance

Linear Codes - Mathematical and Statistical Sciences

parity-check matrix H has minimum distance (exactly) d if and only if every set of d-1 columns of H are linearly independent, and some set of d columns are linearly dependent. Hence this theorem could be used to determine the minimum distance of a …

  Linear, Distance

Metric Spaces - UiO

distance we get when we stop at a third pont z along the way, i.e. d(x,y) ≤ d(x,z)+d(z,x) It turns out that these conditions are the only ones we need, and we sum them up in a formal definition. Definition 1.1.1 A metric space (X,d) consists of a non-empty set X and a function d : …

  Distance

3F3 5 Design of FIR Filters - Vyssotski

10 (1+δ p) dB, or peak-to-peak passband ripple ≅ 20 log 10 (1+2δ p) dB; minimum stopband attenuation = -20 log 10 (δ s) dB. Example: δ p= 6% peak-to-peak passband ripple ≅ 20 log 10 (1+2δ p) = 1dB; δ s = 0.01 minimum stopband attenuation = -20 log 10 (δ s) = 40dB. The band-edge frequencies ω s and ω p are often called corner ...