K-nearest neighbor algorithm

Max GladeCS228 Written ReportFor my final project I decided to expand my KNN-Learner to be able to recognizemovement and gestures that occur over time. I started off working with the ASLalphabet but came to the conclusion that not many signs are dynamic, so I trained myKNN classifier to recognize RSTC (Recreational Scuba Training Council) hand signalswhich mostly require hand movement. On top of requiring the ability to track movementthe KNN classifier and my code needed to be able to handle input from two hands.In order to track movement, each gesture had to be recorded over multipleframes. Just tracking the data points alone turned out to not be enough information forthe KNN learner to accurately classify gestures. Instead I created a second KNNclassifier that took in the change in direction at certain points from one frame to another.This along with the coordinate based KNN classifier seemed to pick up on gesturesfairly accurately.In order to deal with multiple hands being fed to the KNN classifier I needed toexpand my data collection set. Instead of collecting information from 5 fingers i collectedinformation from 10. I placed data from the right hand in the first 5 finger slots and datafrom the right in the other 5. To compensate for blank data fields in the left hand, since Iam right handed and I did not have time to collect left handed data, I mirrored my datasets for one handed gestures in a different instance of a gesture frame. The first major alteration to my code…

The Kriging weights w_i1,w_i2,w_i3,…w_ik can be used to estimate the fair value of VA contracts x ⃑_i by the following formula, y ̂_i=∑_(j=1)^k▒〖w_ij∙y_j 〗. The kriging weights can be calculated by the following linear equations, Where the is a control variable, which is used to make sure that ∑_(j=1)^k▒w_ij =1. V_rs=α+exp⁡(-3/β D(z ⃑_r,z ⃑_s,λ)),r,s=1,2,3,…,k, D_ij=α+exp⁡(-3/β D(x ⃑_i,z ⃑_j,λ)),j=1,2,3,…,k, The D(.) function is the distance function mentioned in the clustering section,α≥0…

Although there are other studies that involves discrimination by the doctor based on patients characteristics or background. A highly reputed doctor is less likely to treat a poor or those who by physical appearance appears weak or if the doctor perceive him/her from a poorer section of the society. The research by William and Neighbors [2003] indicated that discrimination is associated with multiple indicators of the poorer physical and especially mental health status. A paper by Deborah, Lynne…

In this case, electronic noses can detect and classify samples of volatile organic compounds generated by fungal contaminants (Schiffman et al., 2000). Ordinarily, the simplest and most common ANN used for fungal detection is the k-nearest neighbors (KNN) algorithm (Tang, Lin, & Shyu, 2010). However, the KNN algorithm specializes in combined/mixed classes on the principal component analysis (PCA), a graphical, two or three-dimensional representation of the factors (change in resistances of…

synchronization prob- lems in concurrent algorithm design. [1] In the problem there are _ve philosophers. They sit on chairs around a circu- lar table, and they either think or eat spaghetti. On the table there is spaghetti and _ve chopsticks, as shown in the Figure 1. The philosophers think until they are hungry. When a philosopher gets hungry, he tries to take the nearest two chopsticks. If he is able to pick up both of the left and right chopsticks, he eats for sometime. When he _nishes…

112 volunteers on Facebook were chosen who were then asked to complete two surveys online: the big five personality test (BFI-10) and the interpersonal circumplex (IPIP-IPC-32). Candidates with incomplete surveys were disqualified and about a 100 candidates were left. On this data, BoVW was implemented to extract features from images and the results were further refined using SIFT, SVD and VSEM. A t-test was run to compare traits to a set of about 100000 Facebook users randomly sampled from…

A gap-filling method was developed based on the Principal Component Analysis (PCA) method. PCA is a widely used dimensionality reduction method to find a new set of variables as linear combinations of original variables, capturing most of the observed variance in the original data (Storch and Zwiers, 2002). It has been used in various climate studies for decomposing dominant mode (Storch and Zwiers, 2002) and time-series gap-filling (Beckers and Rixen, 2003; Kondrashov et al., 2014). The…

identifying and removing as much of the irrelevant and redundant information as possible from a dataset. If the data dimensions are reduced, we will be able to improve the performance of the applied classification algorithms. They will function faster, more efficiently and will be with an improved classification accuracy. As an addition we can also execute data visualization, and increase the insight of the potential classification model [] . There are many different proposed attribute ranking…

Moreover, with similar assumptions, the authors in [Xue and Kumar 2004] showed that each node should be connected to Θ(logn) nearest neighbors for keeping the network connectivity. The critical conditions for the network asymptotically disconnected and the asymptotically connected are 0.074logn and 5.1774logn, respectively. Further, by considering the deterministically located unreliable nodes, the paper [Shakkottai et al. 2005] shows that, in a unit square area, the necessary and sufficient…