This post would probably be the last in my series about merging R and ArcGIS. In August unfortunately I would have to work for real and I will not have time to play with R-Bridge any more. In this post I would like to present a toolbox to perform some introductory point pattern analysis in R through ArcGIS. Basically, I developed a toolbox to perform the tests I presented in my previous post about point pattern analysis. In there, you can find some theoretical concepts that you need to know to understand what this toolbox can do. I will start by introducing the sample dataset we are going to use, and then simply show the packages available.
A universal unanswered question in neuroscience and machine learning is whether computers can decode the patterns of the human brain. Multi-Voxels Pattern Analysis (MVPA) is a critical tool for addressing this question. However, there are two challenges in the previous MVPA methods, which include decreasing sparsity and noises in the extracted features and increasing the performance of prediction. In overcoming mentioned challenges, this paper proposes Anatomical Pattern Analysis (APA) for decoding visual stimuli in the human brain. This framework develops a novel anatomical feature extraction method and a new imbalance AdaBoost algorithm for binary classification. Further, it utilizes an Error-Correcting Output Codes (ECOC) method for multi-class prediction. APA can automatically detect active regions for each category of the visual stimuli. Moreover, it enables us to combine homogeneous datasets for applying advanced classification. Experimental studies on 4 visual categories (words, consonants, objects and scrambled photos) demonstrate that the proposed approach achieves superior performance to state-of-the-art methods.
Ever wondered why certain countries have certain colors in their flags, why it has certain symbols and what are the different patterns? We started digging through, checking a Wikipedia article on this topic. This was a good start but we wanted to go deeper so we manually started eye balling each flag to understand the patterns and symbols. We needed to figure out what we can extract from the flags and there were three prominent elements. Our task was then to go through each flag and note down all the distinct colors, prominent patterns and symbols.
The purpose of information extraction (IE) systems is to extract domain-specific information from natural language text. IE systems typically rely on two domain-specific resources: a dictionary of extraction patterns and a semantic lexicon. The extraction patterns may be constructed by hand or may be generated automatically using one of several techniques. Most systems that generate extraction patterns automatically use special training resources, such as texts annotated with domain-specific tags (e.g., AutoSlog (Riloff 1993; 1996a), CRYSTAL (Soderland et al. 1995), RAPIER (Califf 1998), SRV (Freitag 1998), WHISK (Soderland 1999)) or manually fined keywords, frames, or object recognizers (e.g., PALKA (Kim & Moldovan 1993) and LIEP (Huffman 1996)). AutoSlog-TS (Riloff 1996b) takes a ferent approach by using a preclassified training corpus in which texts only need to be labeled as relevant In-Copyright Q1999, American Association for Artificial telligence (www.aaai.org).
Editor's Note: A representative from Mashable's Branded Content team attended the Olay panel on a Global Skin Analysis Program at Mobile World Congress and gave a firsthand account of the event. This is where Olay premiered their Skin Advisor, which is a platform powered by artificial intelligence. Mashable's Chief Data Scientist, Haile Owusu, sat on this panel. I'd never really considered the myriad applications of artificial intelligence (AI) until I took my seat at the OLAY panel at Mobile World Congress in Barcelona. The subtle ways that AI is disrupting our lives are many and varied - and often in places you'd least expect.