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Filtering by: Has journal IEEE Communications Letters Contributor Raich, Raviv

Multiple Instance Multiple Label Learning with Limited Supervision

Creator:

Nguyen, Tam

Abstract:

In weak supervision learning, label information can be provided at different levels of granularity. For example, in multi-instance multi-label learning, samples are organized into bags and labels for each class are provided at the bag level. For small datasets, this approach offers means of reducing the labeling efforts. However, in...

Resource Type:

Dissertation

Full Text:

MNIST [38], HJA bird, MSCV2, Voc12, Letter Carol, Letter Frost [4], OSU Wrist [109], Alipr [1], UIUC
Weak-supervision : Probabilistic Models and Inference

Creator:

Pham, Anh

Abstract:

In supervised learning, label information can be provided at different levels of granularity. For small datasets, it is possible to acquire a label for each data instance. However, in the big-data regime, this fine granularity approach is prohibitively costly. For example, in semi-supervised learning, only a limited number of samples...

Resource Type:

Dissertation

Full Text:

Multi-instance Multi-label Learning. IEEE Transactions on PAMI, 2017. TABLE OF CONTENTS (Continued
On Convex Dimensionality Reduction for Classification

Creator:

Tammineni, Tez Deepthi

Abstract:

Dimensionality reduction (DR) is an efficient approach to reduce the size of data by capturing the informative intrinsic features and discarding the noise. DR methods can be grouped through a variety of categories, e.g. supervised/ unsupervised, linear/non-linear or parametric/non-parametric. Objective function based methods can be grouped into convex and non...

Resource Type:

Capstone Project

Full Text:

and extended its applications to various fields such as communication, finance, signal processing and