article thumbnail

A history of tech adaptation for today’s changing business needs

CIO Business Intelligence

Following this, in 2002, it began delivering its knowledge to customers in online format, using dashboards and interactive reports that provided easier and faster access to data and analysis. According to Mohammed, the results of this digital transformation journey are measurable and impressive.

article thumbnail

Fitting Support Vector Machines via Quadratic Programming

Domino Data Lab

Support Vector Machines (SVMs) are supervised learning models with a wide range of applications in text classification (Joachims, 1998), image recognition (Decoste and Schölkopf, 2002), image segmentation (Barghout, 2015), anomaly detection (Schölkopf et al., The use of multiple measurements in taxonomic problems. 1999) and more.

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

ML internals: Synthetic Minority Oversampling (SMOTE) Technique

Domino Data Lab

This renders measures like classification accuracy meaningless. In their 2002 paper Chawla et al. Figure 3 shows visual explanation of how SMOTE generates synthetic observations in this case. 2002) have performed a comprehensive evaluation of the impact of SMOTE- based up-sampling. Generation of artificial examples.

article thumbnail

12 Cloud Computing Risks & Challenges Businesses Are Facing In These Days

datapine

Be it in the form of online BI tools , or an online data visualization system, a company must address where and how to store its data. That’s why it is important to implement a secure BI cloud tool that can leverage proper security measures. Cost management and containment. For the most part, modern computing can save businesses money.

Risk 237
article thumbnail

Themes and Conferences per Pacoid, Episode 10

Domino Data Lab

Her talk addressed career paths for people in data science going into specialized roles, such as data visualization engineers, algorithm engineers, and so on. The most poignant for me was a simple approach for measuring noise within an organization. Measure how these decisions vary across your population.