Videos tagged with Structured data

Google Tech Talk (more info below) August 9, 2011 Presented by Alon Halevy. ABSTRACT: Google hosted 100 attendees of the 2011 conference for the Association of the Advancement of Artificial Intelligence (AAAI) at our San Francisco office. The program showcased a featured talk by Director of Research Peter Norvig and a lightning talk series on an array of projects relevant to the field of artifi...

Google I/O 2010 - BigQuery and Prediction APIs App Engine 101 Amit Agarwal, Max Lin, Gideon Mann, Siddartha Naidu Google relies heavily on data analysis and has developed many tools to understand large datasets. Two of these tools are now available on a limited sign-up basis to developers: (1) BigQuery: interactive analysis of very large data sets and (2) Prediction API: make informed predictio...

Google I/O 2010 - BigQuery and Prediction APIs App Engine 101 Amit Agarwal, Max Lin, Gideon Mann, Siddartha Naidu Google relies heavily on data analysis and has developed many tools to understand large datasets. Two of these tools are now available on a limited sign-up basis to developers: (1) BigQuery: interactive analysis of very large data sets and (2) Prediction API: make informed predictio...

We present a novel method for generic visual categorization: the problem of identifying the object content of natural images while generalizing across variations inherent to the object class. This bag of keypoints method is based on vector quantization of affine invariant descriptors of image patches. We propose and compare two alternative implementations using different classifiers: Naïve...

In this paper we develop a new formulation of probabilistic relaxation labeling for the task of data classification using the theory of diffusion processes on graphs. The state space of our process as the nodes of a support graph which represent potential object-label assignments. The edge-weights of the support graph encode data-proximity and label consistency information. The state-vector of ...

The field of statistical pattern recognition is characterized by the use of feature vectors for pattern representation, while strings or, more generally, graphs are prevailing in structural pattern recognition. In this paper we aim at bridging the gap between the domain of feature based and graph based object representation. We propose a general approach for transforming graphs into n-dimension...

Sequence classification is a significant problem that arises in many different real-world applications. The purpose of a sequence classifier is to assign a class label to a given sequence. Also, to obtain the pattern that characterizes the sequence is usually very useful. In this paper, a technique to discover a pattern from a given sequence is presented followed by a general novel method to cl...

Graph data is getting increasingly popular in, e.g., bioinfor- matics and text processing. A main dificulty of graph data processing lies in the intrinsic high dimensionality of graphs, namely, when a graph is represented as a binary feature vector of indicators of all possible sub- graphs, the dimensionality gets too large for usual statistical methods. Author: Koji Tsuda, Max Planck Institute...

In this paper we study asymmetric proximity measures on directed graphs, which quantify the relationships between two nodes or two groups of nodes. The measures are useful in several graph mining tasks, including clustering, link prediction and connection subgraph discovery. Our proximity measure is based on the concept of escape probability. This way, we strive to summarize the multiple facets...

Lecture slides: Learning CRFs with Hierarchical Features: An Application to Go The Game of Go Territory Prediction Talk Outline Hierarchical Patterns Models Independent Pattern-based Classifiers Inference and Training Bayesian Model Averaging Hierarchical Tree Models CRF & Pattern CRF Inference and Training Pseudolikelihood Local Training Evaluation Models & Algorithms Training Time Inf...