A review on the combination of binary classifiers in multiclass problems
Several real problems involve the classification of data into categories or classes. Given a
data set containing data whose classes are known, Machine Learning algorithms can be …
data set containing data whose classes are known, Machine Learning algorithms can be …
Probabilistic machine learning for healthcare
Machine learning can be used to make sense of healthcare data. Probabilistic machine
learning models help provide a complete picture of observed data in healthcare. In this …
learning models help provide a complete picture of observed data in healthcare. In this …
From softmax to sparsemax: A sparse model of attention and multi-label classification
A Martins, R Astudillo - International conference on machine …, 2016 - proceedings.mlr.press
We propose sparsemax, a new activation function similar to the traditional softmax, but able
to output sparse probabilities. After deriving its properties, we show how its Jacobian can be …
to output sparse probabilities. After deriving its properties, we show how its Jacobian can be …
An ensemble of fine-tuned convolutional neural networks for medical image classification
The availability of medical imaging data from clinical archives, research literature, and
clinical manuals, coupled with recent advances in computer vision offer the opportunity for …
clinical manuals, coupled with recent advances in computer vision offer the opportunity for …
Unified classification of mouse retinal ganglion cells using function, morphology, and gene expression
Classification and characterization of neuronal types are critical for understanding their
function and dysfunction. Neuronal classification schemes typically rely on measurements of …
function and dysfunction. Neuronal classification schemes typically rely on measurements of …
Transforming classifier scores into accurate multiclass probability estimates
B Zadrozny, C Elkan - Proceedings of the eighth ACM SIGKDD …, 2002 - dl.acm.org
Class membership probability estimates are important for many applications of data mining
in which classification outputs are combined with other sources of information for decision …
in which classification outputs are combined with other sources of information for decision …
[图书][B] Multi-sensor data fusion: an introduction
HB Mitchell - 2007 - books.google.com
The purpose of this book is to provide an introduction to the theories and techniques of multi-
sensor data fusion. The book has been designed as a text for a one-semester graduate …
sensor data fusion. The book has been designed as a text for a one-semester graduate …
Systems and methods for mobile image capture and processing
A Macciola, A Shustorovich, CW Thrasher - US Patent 8,855,375, 2014 - Google Patents
2022-07-20 Assigned to CREDIT SUISSE AG, CAYMAN ISLANDS BRANCH, AS
COLLATERAL AGENT reassignment CREDIT SUISSE AG, CAYMAN ISLANDS BRANCH …
COLLATERAL AGENT reassignment CREDIT SUISSE AG, CAYMAN ISLANDS BRANCH …
Distribution calibration for regression
We are concerned with obtaining well-calibrated output distributions from regression
models. Such distributions allow us to quantify the uncertainty that the model has regarding …
models. Such distributions allow us to quantify the uncertainty that the model has regarding …
Using discriminant analysis for multi-class classification: an experimental investigation
Many supervised machine learning tasks can be cast as multi-class classification problems.
Support vector machines (SVMs) excel at binary classification problems, but the elegant …
Support vector machines (SVMs) excel at binary classification problems, but the elegant …