Papers Multi-Label Classification

Self-Organizing Maps for Classification of a Multi-Labeled Corpus

Exploring Correlation between Labels to improve Multi-Label Classification

This paper attempts multi-label classification by extending the idea of independent binary classification models for each output label, and exploring how the inherent correlation between output labels can be used to impr…

Structured Prediction Energy Networks

We introduce structured prediction energy networks (SPENs), a flexible framework for structured prediction. A deep architecture is used to define an energy function of candidate labels, and then predictions are produced …

Fast Metric Learning For Deep Neural Networks

Similarity metrics are a core component of many information retrieval and machine learning systems. In this work we propose a method capable of learning a similarity metric from data equipped with a binary relation. By c…

Normalization of Relative and Incomplete Temporal Expressions in Clinical Narratives

We analyze the RI-TIMEXes in temporally annotated corpora and propose two hypotheses regarding the normalization of RI-TIMEXes in the clinical narrative domain: the anchor point hypothesis and the anchor relation hypothe…

Locally Non-linear Embeddings for Extreme Multi-label Learning

The objective in extreme multi-label learning is to train a classifier that can automatically tag a novel data point with the most relevant subset of labels from an extremely large label set. Embedding based approaches m…

Sentence-level Emotion Classification with Label and Context Dependence

An Ensemble method for Content Selection for Data-to-text Systems

We present a novel approach for automatic report generation from time-series data, in the context of student feedback generation. Our proposed methodology treats content selection as a multi-label classification (MLC) pr…

Semidefinite and Spectral Relaxations for Multi-Label Classification

In this paper, we address the problem of multi-label classification. We consider linear classifiers and propose to learn a prior over the space of labels to directly leverage the performance of such methods. This prior t…

Semi-Supervised Low-Rank Mapping Learning for Multi-Label Classification

Multi-label problems arise in various domains including automatic multimedia data categorization, and have generated significant interest in computer vision and machine learning community. However, existing methods do n…

Surrogate Functions for Maximizing Precision at the Top

The problem of maximizing precision at the top of a ranked list, often dubbed Precision@k (prec@k), finds relevance in myriad learning applications such as ranking, multi-label classification, and learning with severe la…

Dense Distributions from Sparse Samples: Improved Gibbs Sampling Parameter Estimators for LDA

We introduce a novel approach for estimating Latent Dirichlet Allocation (LDA) parameters from collapsed Gibbs samples (CGS), by leveraging the full conditional distributions over the latent variable assignments to effic…

Exploit Bounding Box Annotations for Multi-label Object Recognition

Convolutional neural networks (CNNs) have shown great performance as general feature representations for object recognition applications. However, for multi-label images that contain multiple objects from different categ…

Multi-label Classification using Labels as Hidden Nodes

Competitive methods for multi-label classification typically invest in learning labels together. To do so in a beneficial way, analysis of label dependence is often seen as a fundamental step, separate and prior to const…

Counterfactual Risk Minimization: Learning from Logged Bandit Feedback

We develop a learning principle and an efficient algorithm for batch learning from logged bandit feedback. This learning setting is ubiquitous in online systems (e.g., ad placement, web search, recommendation), where an …

Scalable Multi-Output Label Prediction: From Classifier Chains to Classifier Trellises

Multi-output inference tasks, such as multi-label classification, have become increasingly important in recent years. A popular method for multi-label classification is classifier chains, in which the predictions of indi…

oAdaBoost: An AdaBoost Variant for Ordinal Classification

Ordinal data classification (ODC) has a wide range of applications in areas where human evaluation plays an important role, ranging from psychology and medicine to information retrieval. In ODC the output variable has a…

On Learning Vector Representations in Hierarchical Label Spaces

An important problem in multi-label classification is to capture label patterns or underlying structures that have an impact on such patterns. This paper addresses one such problem, namely how to exploit hierarchical str…

Scoring and Classifying with Gated Auto-encoders

Auto-encoders are perhaps the best-known non-probabilistic methods for representation learning. They are conceptually simple and easy to train. Recent theoretical work has shed light on their ability to capture manifold …

Deep Learning for Multi-label Classification

In multi-label classification, the main focus has been to develop ways of learning the underlying dependencies between labels, and to take advantage of this at classification time. Developing better feature-space represe…