76 research outputs found
Challenge IEEE-ISBI/TCB : Application of Covariance matrices and wavelet marginals
This short memo aims at explaining our approach for the challenge IEEE-ISBI
on Bone Texture Characterization. In this work, we focus on the use of
covariance matrices and wavelet marginals in an SVM classifier.Comment: 9 pages, 4 Figues, 2 Tables, Challenge IEEE-ISBI : Bone Texture
Characterizatio
Challenges in anomaly and change point detection
This paper presents an introduction to the state-of-the-art in anomaly and
change-point detection. On the one hand, the main concepts needed to understand
the vast scientific literature on those subjects are introduced. On the other,
a selection of important surveys and books, as well as two selected active
research topics in the field, are presented
Using Posters to Recommend Anime and Mangas in a Cold-Start Scenario
Item cold-start is a classical issue in recommender systems that affects
anime and manga recommendations as well. This problem can be framed as follows:
how to predict whether a user will like a manga that received few ratings from
the community? Content-based techniques can alleviate this issue but require
extra information, that is usually expensive to gather. In this paper, we use a
deep learning technique, Illustration2Vec, to easily extract tag information
from the manga and anime posters (e.g., sword, or ponytail). We propose BALSE
(Blended Alternate Least Squares with Explanation), a new model for
collaborative filtering, that benefits from this extra information to recommend
mangas. We show, using real data from an online manga recommender system called
Mangaki, that our model improves substantially the quality of recommendations,
especially for less-known manga, and is able to provide an interpretation of
the taste of the users.Comment: 6 pages, 3 figures, 1 table, accepted at the MANPU 2017 workshop,
co-located with ICDAR 2017 in Kyoto on November 10, 201
Uplift Modeling from Separate Labels
Uplift modeling is aimed at estimating the incremental impact of an action on an individual's behavior, which is useful in various application domains such as targeted marketing (advertisement campaigns) and personalized medicine (medical treatments). Conventional methods of uplift modeling require every instance to be jointly equipped with two types of labels: the taken action and its outcome. However, obtaining two labels for each instance at the same time is difficult or expensive in many real-world problems. In this paper, we propose a novel method of uplift modeling that is applicable to a more practical setting where only one type of labels is available for each instance. We show a mean squared error bound for the proposed estimator and demonstrate its effectiveness through experiments
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