Add to ORKGWe describe a nonparametric Bayesian approach to generalizing from few labeled examples, guided by a larger set of unlabeled objects and the assumption of a latent tree-structure to the domain. The tree (or a distribution over trees) may be inferred using the unlabeled data. A prior over concepts generated by a mutation process on the inferred tree(s) allows efficient computation of the optimal Bayesian classification function from the labeled examples. We test our approach on eight real-world datasets
We consider the general problem of learning from labeled and unlabeled data, which is often called s...
In this dissertation, we explore two fundamental sets of inference problems arising in machine learn...
Indiana University-Purdue University Indianapolis (IUPUI)Robustness and generalizability of supervis...
In many real-life problems, obtaining labelled data can be a very expensive and laborious task, whil...
Robustness and generalizability of supervised learning algorithms depend on the quality of the label...
We propose a simple taxonomy of probabilistic graphical models for the semi-supervised learning prob...
Classification has been tackled by a large number of algorithms, predominantly following a supervise...
Semi-supervised learning (SSL), is classification where additional unlabeled data can be used to imp...
We consider semi-supervised learning, learning task from both labeled and unlabeled instances and in...
We consider the general problem of learning from labeled and unlabeled data, which is often called s...
This paper presents a classifier which uses a tree-based Neural Network (NN), and uses both, unlabel...
Tree augmented naive Bayes is a semi-naive Bayesian Learning method. It relaxes the naive Bayes attr...
We consider the general problem of learning from labeled and unlabeled data, which is often called s...
Algorithms for learning classification trees have had successes in artificial intelligence and stati...
The ever-increasing size of modern data sets combined with the difficulty of obtaining label informa...
We consider the general problem of learning from labeled and unlabeled data, which is often called s...
In this dissertation, we explore two fundamental sets of inference problems arising in machine learn...
Indiana University-Purdue University Indianapolis (IUPUI)Robustness and generalizability of supervis...
In many real-life problems, obtaining labelled data can be a very expensive and laborious task, whil...
Robustness and generalizability of supervised learning algorithms depend on the quality of the label...
We propose a simple taxonomy of probabilistic graphical models for the semi-supervised learning prob...
Classification has been tackled by a large number of algorithms, predominantly following a supervise...
Semi-supervised learning (SSL), is classification where additional unlabeled data can be used to imp...
We consider semi-supervised learning, learning task from both labeled and unlabeled instances and in...
We consider the general problem of learning from labeled and unlabeled data, which is often called s...
This paper presents a classifier which uses a tree-based Neural Network (NN), and uses both, unlabel...
Tree augmented naive Bayes is a semi-naive Bayesian Learning method. It relaxes the naive Bayes attr...
We consider the general problem of learning from labeled and unlabeled data, which is often called s...
Algorithms for learning classification trees have had successes in artificial intelligence and stati...
The ever-increasing size of modern data sets combined with the difficulty of obtaining label informa...
We consider the general problem of learning from labeled and unlabeled data, which is often called s...
In this dissertation, we explore two fundamental sets of inference problems arising in machine learn...
Indiana University-Purdue University Indianapolis (IUPUI)Robustness and generalizability of supervis...