Zobrazeno 1 - 10
of 56
pro vyhledávání: '"Ackerman, Margareta"'
The allocation of venture capital is one of the primary factors determining who takes products to market, which startups succeed or fail, and as such who gets to participate in the shaping of our collective economy. While gender diversity contributes
Externí odkaz:
http://arxiv.org/abs/2101.12008
Clustering is a widely-used data mining tool, which aims to discover partitions of similar items in data. We introduce a new clustering paradigm, \emph{accordant clustering}, which enables the discovery of (predefined) group level insights. Unlike pr
Externí odkaz:
http://arxiv.org/abs/1704.02378
Publikováno v:
In Pattern Recognition December 2021 120
Autor:
Ackerman, Margareta, Loker, David
This paper introduces ALYSIA: Automated LYrical SongwrIting Application. ALYSIA is based on a machine learning model using Random Forests, and we discuss its success at pitch and rhythm prediction. Next, we show how ALYSIA was used to create original
Externí odkaz:
http://arxiv.org/abs/1612.01058
Clustering is an essential data mining tool that aims to discover inherent cluster structure in data. As such, the study of clusterability, which evaluates whether data possesses such structure, is an integral part of cluster analysis. Yet, despite t
Externí odkaz:
http://arxiv.org/abs/1602.06687
Autor:
Ackerman, Margareta, Moore, Jarrod
Clustering is a fundamental data mining tool that aims to divide data into groups of similar items. Generally, intuition about clustering reflects the ideal case -- exact data sets endowed with flawless dissimilarity between individual instances. In
Externí odkaz:
http://arxiv.org/abs/1601.05900
Autor:
Ackerman, Margareta
Clustering is a central unsupervised learning task with a wide variety of applications. Unlike in supervised learning, different clustering algorithms may yield dramatically different outputs for the same input sets. As such, the choice of algorithm
Externí odkaz:
http://hdl.handle.net/10012/6824
Autor:
Ackerman, Margareta
Clustering is a widely used technique, with applications ranging from data mining, bioinformatics and image analysis to marketing, psychology, and city planning. Despite the practical importance of clustering, there is very limited theoretical analys
Externí odkaz:
http://hdl.handle.net/10012/3478
Autor:
Ackerman, Margareta, Dasgupta, Sanjoy
The explosion in the amount of data available for analysis often necessitates a transition from batch to incremental clustering methods, which process one element at a time and typically store only a small subset of the data. In this paper, we initia
Externí odkaz:
http://arxiv.org/abs/1406.6398
Publikováno v:
In Cognitive Systems Research May 2019 54:199-216