896 research outputs found
Evaluation of Output Embeddings for Fine-Grained Image Classification
Image classification has advanced significantly in recent years with the
availability of large-scale image sets. However, fine-grained classification
remains a major challenge due to the annotation cost of large numbers of
fine-grained categories. This project shows that compelling classification
performance can be achieved on such categories even without labeled training
data. Given image and class embeddings, we learn a compatibility function such
that matching embeddings are assigned a higher score than mismatching ones;
zero-shot classification of an image proceeds by finding the label yielding the
highest joint compatibility score. We use state-of-the-art image features and
focus on different supervised attributes and unsupervised output embeddings
either derived from hierarchies or learned from unlabeled text corpora. We
establish a substantially improved state-of-the-art on the Animals with
Attributes and Caltech-UCSD Birds datasets. Most encouragingly, we demonstrate
that purely unsupervised output embeddings (learned from Wikipedia and improved
with fine-grained text) achieve compelling results, even outperforming the
previous supervised state-of-the-art. By combining different output embeddings,
we further improve results.Comment: @inproceedings {ARWLS15, title = {Evaluation of Output Embeddings for
Fine-Grained Image Classification}, booktitle = {IEEE Computer Vision and
Pattern Recognition}, year = {2015}, author = {Zeynep Akata and Scott Reed
and Daniel Walter and Honglak Lee and Bernt Schiele}
Automated classification of web contents in B2B marketing
Recent growth in digitization has affected how customers seek the information they need to make a purchase decision. This trend of customers making their purchase decision based on the information they collect online is increasing. To accommodate this change in purchase behavior, companies tend to share as much information about themselves and their products online, which in turn drives the amount of unstructured data produced. To get value for this huge amount of data being produced, the unstructured data needs to be processed before being used in digital marketing applications. When it comes to the companies serving business to customers (B2C), plenty of research exists on how the digital content could be used for marketing, but for the companies serving business to business (B2B) a huge research gap presides. B2C marketing and B2B marketing might share some analytical concepts but they are different domains. Not much research has been done in the field of using machine learning in B2B digital marketing. The lack of availability of labeled text data from the B2B domain makes it challenging for researchers to experiment on text classification models, while several methods have been proposed and used to classify unstructured text data in marketing and other domains.
This thesis studies previous works done in the field of text classification in general, in the marketing domain, and compares those methods across the dataset available for this research.
Text classification methods such as Random Forest, Linear SVM, KNN, Multinomial NaĂŻve Bayes, and Multinomial Logistic Regression dominates the research field, hence these methods are tested in this research. In the used dataset surprisingly, Random Forest Classifier performed best with an average accuracy of 0.85 in the designed five-class classification task
Mutual Exclusivity Loss for Semi-Supervised Deep Learning
In this paper we consider the problem of semi-supervised learning with deep
Convolutional Neural Networks (ConvNets). Semi-supervised learning is motivated
on the observation that unlabeled data is cheap and can be used to improve the
accuracy of classifiers. In this paper we propose an unsupervised
regularization term that explicitly forces the classifier's prediction for
multiple classes to be mutually-exclusive and effectively guides the decision
boundary to lie on the low density space between the manifolds corresponding to
different classes of data. Our proposed approach is general and can be used
with any backpropagation-based learning method. We show through different
experiments that our method can improve the object recognition performance of
ConvNets using unlabeled data.Comment: 5 pages, 1 figures, ICIP 201
Dataless text classification with descriptive LDA
Manually labeling documents for training a text classifier is expensive and time-consuming. Moreover, a classifier trained on labeled documents may suffer from overfitting and adaptability problems. Dataless text classification (DLTC) has been proposed as a solution to these problems, since it does not require labeled documents. Previous research in DLTC has used explicit semantic analysis of Wikipedia content to measure semantic distance between documents, which is in turn used to classify test documents based on nearest neighbours. The semantic-based DLTC method has a major drawback in that it relies on a large-scale, finely-compiled semantic knowledge base, which is difficult to obtain in many scenarios. In this paper we propose a novel kind of model, descriptive LDA (DescLDA), which performs DLTC with only category description words and unlabeled documents. In DescLDA, the LDA model is assembled with a describing device to infer Dirichlet priors from prior descriptive documents created with category description words. The Dirichlet priors are then used by LDA to induce category-aware latent topics from unlabeled documents. Experimental results with the 20Newsgroups and RCV1 datasets show that: (1) our DLTC method is more effective than the semantic-based DLTC baseline method; and (2) the accuracy of our DLTC method is very close to state-of-the-art supervised text classification methods. As neither external knowledge resources nor labeled documents are required, our DLTC method is applicable to a wider range of scenarios
A Convex Relaxation for Weakly Supervised Classifiers
This paper introduces a general multi-class approach to weakly supervised
classification. Inferring the labels and learning the parameters of the model
is usually done jointly through a block-coordinate descent algorithm such as
expectation-maximization (EM), which may lead to local minima. To avoid this
problem, we propose a cost function based on a convex relaxation of the
soft-max loss. We then propose an algorithm specifically designed to
efficiently solve the corresponding semidefinite program (SDP). Empirically,
our method compares favorably to standard ones on different datasets for
multiple instance learning and semi-supervised learning as well as on
clustering tasks.Comment: Appears in Proceedings of the 29th International Conference on
Machine Learning (ICML 2012
Multiclass Data Segmentation using Diffuse Interface Methods on Graphs
We present two graph-based algorithms for multiclass segmentation of
high-dimensional data. The algorithms use a diffuse interface model based on
the Ginzburg-Landau functional, related to total variation compressed sensing
and image processing. A multiclass extension is introduced using the Gibbs
simplex, with the functional's double-well potential modified to handle the
multiclass case. The first algorithm minimizes the functional using a convex
splitting numerical scheme. The second algorithm is a uses a graph adaptation
of the classical numerical Merriman-Bence-Osher (MBO) scheme, which alternates
between diffusion and thresholding. We demonstrate the performance of both
algorithms experimentally on synthetic data, grayscale and color images, and
several benchmark data sets such as MNIST, COIL and WebKB. We also make use of
fast numerical solvers for finding the eigenvectors and eigenvalues of the
graph Laplacian, and take advantage of the sparsity of the matrix. Experiments
indicate that the results are competitive with or better than the current
state-of-the-art multiclass segmentation algorithms.Comment: 14 page
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